XgBoostRegressor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18e35041-51a1-4917-8a1e-290378d8009e",
   "metadata": {},
   "source": [
    "### Importing necessary libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fde0b55-ed52-4677-a7b9-778dc965b769",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66211f2-bde2-4d41-96c6-548fbb935341",
   "metadata": {},
   "source": [
    "#### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cc12eca-d098-4c1f-9d27-341e1c9361f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"AQI_Data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f60ba874-9e5a-46c7-b423-4f4a172583b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2d6b24-7229-4d74-83f7-342ef7690362",
   "metadata": {},
   "source": [
    "#### Dropping the one & only missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "337700e9-fe2c-401b-bfa3-ab3e53c2531e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28c01ad7-c7d4-46b5-bb8d-21ae5b532414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e820261e-c922-4bba-9215-bf4b606066b0",
   "metadata": {},
   "source": [
    "#### Separating data & features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79638fbc-2b3c-4b15-b533-f323dc98a992",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,:-1] ## independent features\n",
    "Y=data.iloc[:,-1]  ## dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0e5d1c8-8091-416c-a86a-68098bce4808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T      0\n",
       "TM     0\n",
       "Tm     0\n",
       "SLP    0\n",
       "H      0\n",
       "VV     0\n",
       "V      0\n",
       "VM     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f03c8a2-f946-46ee-9c7f-98800753f1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde64c46-fcd7-4c38-a0f7-b36f8a16c351",
   "metadata": {},
   "source": [
    "#### Conducting feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2641b3c-ca59-4cd8-af7a-dbe6a59eacbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ExtraTreesRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9efe5d20-c083-40d0-9fb2-ac554fba0128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70a64788-42b6-4f74-9cee-000da9e679d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.18807346 0.10532941 0.17841102 0.15476225 0.08759398 0.19064349\n",
      " 0.05408535 0.04110103]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0b6f2f16-7c52-4c14-96d3-542709142ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(8).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1bc308f-7f77-4d65-a574-1c515c4152b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kanch\\AppData\\Local\\Temp\\ipykernel_8888\\3269117342.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(Y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f740ed4-7c42-4a89-8c82-1133a3bfb687",
   "metadata": {},
   "source": [
    "### Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6040e83e-91c1-4216-98f0-604795aa0a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c195d716-c9ba-4b4f-84f2-695669d2a589",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4623f9a3-8dbd-448d-b454-2c983c4cdb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ed77dd0-e60d-41e0-af91-9c8b7f3d3294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor=xgb.XGBRegressor()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f15df920-6c8b-449e-a31b-68015f75e61c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination R^2 <-- on train set: 0.9998008668280981\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of determination R^2 <-- on train set: {}\".format(regressor.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65754341-8bdd-441b-a24e-4a72bd79c70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination R^2 <-- on train set: 0.7789672842036623\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of determination R^2 <-- on train set: {}\".format(regressor.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a85c4f-f2ea-4b0a-bc4f-532035ddff0e",
   "metadata": {},
   "source": [
    "### Performing K fold CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "217410ee-38ca-4368-9ed3-b4e3cc734fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(regressor,X,Y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8fb58c96-1994-4e15-a8a8-80a13ae37b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744970110899368"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9a2c0dc-1919-4626-95ed-1298d0777708",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43acf775-4b39-4443-b6cb-0684c477ac61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kanch\\AppData\\Local\\Temp\\ipykernel_8888\\1520944062.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-prediction)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b58b04-b49b-4eb6-9a7a-f8e21caa0502",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ffce412-6fc5-4baa-92d5-3797b04e6d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "19208b14-b847-4bba-9685-26dd7dd48cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0d92c3cc-8849-4639-9151-24ef6105ee91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d38570b4-f12b-4f2c-8320-4b9545cbd107",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Various learning rate parameters\n",
    "learning_rate = ['0.05','0.1', '0.2','0.3','0.5','0.6']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "#Subssample parameter values\n",
    "subsample=[0.7,0.6,0.8]\n",
    "# Minimum child weight parameters\n",
    "min_child_weight=[3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6cd5c453-5d74-43e8-8453-dcdbfc71ad81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'learning_rate': ['0.05', '0.1', '0.2', '0.3', '0.5', '0.6'], 'max_depth': [5, 10, 15, 20, 25, 30], 'subsample': [0.7, 0.6, 0.8], 'min_child_weight': [3, 4, 5, 6, 7]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'learning_rate': learning_rate,\n",
    "               'max_depth': max_depth,\n",
    "               'subsample': subsample,\n",
    "               'min_child_weight': min_child_weight}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bd7020ab-c791-4c34-b744-b44799425644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "regressor=xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a1fbdef-eed2-4848-a0dc-b1f0d5beee5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_random = RandomizedSearchCV(estimator = regressor, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 500, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "457907be-7164-4550-8896-e845c00b8867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 500 candidates, totalling 2500 fits\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=800, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=800, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=800, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=800, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=800, subsample=0.6; total time=   2.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   3.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   2.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   2.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   3.0s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   2.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   2.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   3.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   2.5s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   2.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   2.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=7, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   3.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   3.2s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   2.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=700, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=700, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=700, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=700, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=700, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=900, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=900, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=900, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=3, n_estimators=900, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=900, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=800, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=800, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=800, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=800, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=3, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=6, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   2.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   2.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   2.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   2.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   2.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=1100, subsample=0.6; total time=   3.5s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   5.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   4.1s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   4.1s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   3.5s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   2.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=600, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.7; total time=   2.0s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=3, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=100, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   2.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   2.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   2.5s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   2.5s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=600, subsample=0.7; total time=   2.4s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=1200, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=1200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=1200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=1200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=4, n_estimators=1200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=500, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=1000, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=200, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.6s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=6, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=600, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1000, subsample=0.8; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1000, subsample=0.8; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1000, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1000, subsample=0.8; total time=   2.3s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1000, subsample=0.8; total time=   2.2s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=200, subsample=0.8; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=800, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=5, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=4, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=4, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=4, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=4, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=4, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=5, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=600, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=5, n_estimators=1100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=3, n_estimators=500, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=700, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=700, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=700, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=700, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=700, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=7, n_estimators=1200, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=1200, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=1200, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=1200, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=1200, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=3, n_estimators=1200, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=4, n_estimators=1100, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=7, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=700, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=700, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=700, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=700, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=4, n_estimators=700, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   3.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   2.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   2.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   2.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=700, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=700, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=700, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=700, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=700, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=5, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=700, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=800, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=800, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   2.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   2.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   2.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   2.9s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   2.2s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.7s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=700, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=800, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=4, n_estimators=200, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=6, n_estimators=1200, subsample=0.6; total time=   2.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   2.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   2.8s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   2.7s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   3.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.8; total time=   2.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=5, n_estimators=1000, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=6, n_estimators=400, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=100, subsample=0.7; total time=   0.0s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=25, min_child_weight=3, n_estimators=500, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=3, n_estimators=1200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=700, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=700, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=700, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=6, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=20, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=10, min_child_weight=7, n_estimators=600, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=300, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   2.0s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=1200, subsample=0.7; total time=   1.9s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=7, n_estimators=500, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=1100, subsample=0.7; total time=   2.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1200, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=5, min_child_weight=3, n_estimators=500, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=200, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.4s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.3s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.2s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   3.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=3, n_estimators=900, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=7, n_estimators=700, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=7, n_estimators=300, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   3.5s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   2.4s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   2.6s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   2.7s\n",
      "[CV] END learning_rate=0.05, max_depth=30, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   2.2s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=20, min_child_weight=6, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.6, max_depth=10, min_child_weight=5, n_estimators=900, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=4, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=6, n_estimators=400, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.4s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=3, n_estimators=400, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   2.0s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   1.8s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   2.1s\n",
      "[CV] END learning_rate=0.1, max_depth=25, min_child_weight=5, n_estimators=800, subsample=0.7; total time=   2.0s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.5, max_depth=5, min_child_weight=3, n_estimators=600, subsample=0.7; total time=   0.2s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=5, min_child_weight=5, n_estimators=800, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=6, n_estimators=1100, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=3, n_estimators=400, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=4, n_estimators=800, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=400, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.1, max_depth=5, min_child_weight=4, n_estimators=500, subsample=0.6; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.3s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.6, max_depth=30, min_child_weight=6, n_estimators=100, subsample=0.8; total time=   0.2s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=100, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=7, n_estimators=900, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.7s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.6, max_depth=15, min_child_weight=6, n_estimators=1200, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=6, n_estimators=200, subsample=0.6; total time=   0.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=5, n_estimators=500, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=3, n_estimators=1100, subsample=0.7; total time=   1.7s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=15, min_child_weight=3, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=3, n_estimators=200, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=4, n_estimators=400, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=5, n_estimators=1000, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.2s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=25, min_child_weight=3, n_estimators=1200, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.8; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=6, n_estimators=900, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.9s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.4s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   2.0s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=7, n_estimators=800, subsample=0.8; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   0.8s\n",
      "[CV] END learning_rate=0.3, max_depth=15, min_child_weight=4, n_estimators=1100, subsample=0.7; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=300, subsample=0.8; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.05, max_depth=20, min_child_weight=7, n_estimators=1000, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.05, max_depth=10, min_child_weight=3, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.2s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.3, max_depth=20, min_child_weight=4, n_estimators=1000, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=30, min_child_weight=4, n_estimators=300, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=10, min_child_weight=6, n_estimators=600, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   2.2s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   2.0s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   1.8s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   1.6s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   1.7s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=5, n_estimators=700, subsample=0.6; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=5, min_child_weight=7, n_estimators=900, subsample=0.6; total time=   0.5s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.1s\n",
      "[CV] END learning_rate=0.05, max_depth=15, min_child_weight=5, n_estimators=100, subsample=0.7; total time=   0.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.3, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   1.5s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.3s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.1, max_depth=20, min_child_weight=6, n_estimators=600, subsample=0.7; total time=   1.4s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   0.9s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.3, max_depth=25, min_child_weight=4, n_estimators=1100, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.2, max_depth=5, min_child_weight=4, n_estimators=1000, subsample=0.8; total time=   0.7s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   2.4s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   2.3s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   2.6s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   4.1s\n",
      "[CV] END learning_rate=0.1, max_depth=30, min_child_weight=6, n_estimators=900, subsample=0.7; total time=   5.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   1.1s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.7s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=30, min_child_weight=5, n_estimators=300, subsample=0.6; total time=   0.9s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   1.9s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   2.1s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   2.4s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   3.0s\n",
      "[CV] END learning_rate=0.2, max_depth=15, min_child_weight=6, n_estimators=900, subsample=0.6; total time=   2.5s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   0.8s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=700, subsample=0.6; total time=   1.0s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.6s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.5s\n",
      "[CV] END learning_rate=0.3, max_depth=10, min_child_weight=7, n_estimators=400, subsample=0.7; total time=   0.4s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   3.1s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   3.3s\n",
      "[CV] END learning_rate=0.05, max_depth=25, min_child_weight=3, n_estimators=1000, subsample=0.7; total time=   3.0s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.0s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.1s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   0.8s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.3s\n",
      "[CV] END learning_rate=0.5, max_depth=25, min_child_weight=7, n_estimators=1200, subsample=0.8; total time=   1.6s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,\n",
       "                                          importance_type=None,\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=...\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=None, ...),\n",
       "                   n_iter=500, n_jobs=1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [&#x27;0.05&#x27;, &#x27;0.1&#x27;, &#x27;0.2&#x27;,\n",
       "                                                          &#x27;0.3&#x27;, &#x27;0.5&#x27;, &#x27;0.6&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;min_child_weight&#x27;: [3, 4, 5, 6, 7],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200],\n",
       "                                        &#x27;subsample&#x27;: [0.7, 0.6, 0.8]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,\n",
       "                                          importance_type=None,\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=...\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=None, ...),\n",
       "                   n_iter=500, n_jobs=1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [&#x27;0.05&#x27;, &#x27;0.1&#x27;, &#x27;0.2&#x27;,\n",
       "                                                          &#x27;0.3&#x27;, &#x27;0.5&#x27;, &#x27;0.6&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;min_child_weight&#x27;: [3, 4, 5, 6, 7],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200],\n",
       "                                        &#x27;subsample&#x27;: [0.7, 0.6, 0.8]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,\n",
       "                                          importance_type=None,\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=...\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=None, ...),\n",
       "                   n_iter=500, n_jobs=1,\n",
       "                   param_distributions={'learning_rate': ['0.05', '0.1', '0.2',\n",
       "                                                          '0.3', '0.5', '0.6'],\n",
       "                                        'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'min_child_weight': [3, 4, 5, 6, 7],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200],\n",
       "                                        'subsample': [0.7, 0.6, 0.8]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebb45ce-6542-4d3c-8af6-2cfb78e34bac",
   "metadata": {},
   "source": [
    "#### best params obtained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf592843-4789-4adb-8f5b-ee3e833e2008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.6,\n",
       " 'n_estimators': 800,\n",
       " 'min_child_weight': 5,\n",
       " 'max_depth': 20,\n",
       " 'learning_rate': '0.2'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f45451ec-90fc-4269-be70-760f916556cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1599.7927639575032"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4412acdf-a7c8-4e34-b367-00a6f7aceedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1599.7927639575032"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8d358fe7-e01c-4bf6-89fc-0f951fc62316",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=xg_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d2731388-45c9-4952-acfe-9fe15224a3f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kanch\\AppData\\Local\\Temp\\ipykernel_8888\\2131792714.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-predictions)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cc6f60bd-920f-45fe-bcfc-3570161f635c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1edaa88ee10>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KFeagEJGqTB+ciSk5RlaSleCr2muyju+cEI3C24fgtuE3wGYXkGlIgNnS4DIPRYfWwJBTahQqDFCISHWio3QwsYqpV71SkyQdd/vwLPx0VE+nQE+cUsvbeAw6OO+6wyk1UgNO8RARadR8U294ix+idMDzPxkGU9+0DsEGp9RIzTiCQkSkUXExUVg8IRt/OeA+UXbxhGzExbj/LcopNVIrBihERBpWMLN15VP7OihRutbgRLzfE06pkRrpBKH9VlLqZ7VaYTAYYLFYoNfrQ90cIqKQEyvJflV7Db1SkzDf1NvjyAlRKMg5f3MEhYioDZtdCOl0h6+BRlxMFBZN6BOEFhIFBwMUIqL/KC6rwupt5U6bFmYaErBqdk5QEkZdlax/uuiU5KkaonDC8T8iIrQGJ3kbj3XYUVncOC/Qu/u6K1lvF4C/HKhEYVF5QF+fSG0YoBBRxLPZBazeVu524zygdeM8my9lWyW89kf/voS/eliJA7QmwTa12BV/fSK1YoBCRBHvSGVth5GTtgK1cV5xWRXGP7cH8/9+xGVw1JZdaC1tTxQpmINCRBFP6oZ4Sm6cJ04pyRmTkVvankjLOIJCRBFP6oZ4Sm2c52lKyROppe2JwgEDFCKKeKOzU5FpSIC7xcQ6tK7mUWrjPG9TSq5E6VpL2xNFCgYoRBTxxI3zAHQIUgKxcZ4vU0XeStYThRvmoBCRqoSqUJq4cV77OihGheqgtC3AJqeAt5yS9UThRFaAUlhYiHfffRdffvklEhMT8cMf/hDPPfccBgwY4Djmlltuwf79+50e9/Of/xxr1651XD9//jzy8vKwd+9edOrUCQsXLkRhYSFiYhgvEUWyUBdKC9TGea4KsHmTFBeNR3L7Y+EPOXJCkUlWRLB//37k5+dj1KhRaGlpwYoVKzB16lSUl5cjOTnZcdzixYvx5JNPOq4nJX2f2GWz2TBr1iwYjUZ88sknqKqqwoIFCxAbG4tnnnlGgS4RkRa5W9UiFkpbc++IoAQpSm+cJxZgk0oMhf7wX8OC0l8itfJrs8BLly4hPT0d+/fvx8SJEwG0jqAMHz4cL730ksvH7NixA7fddhsuXryIjIwMAMDatWuxfPlyXLp0CXFxcV5fl5sFEmlb+2mckb264Ecv7HWbOKpD61TLweWTgrovjr+aWuwY+NgOWSMnwRwxIgq2oG0WaLFYAACpqc6Z7W+99RY2btwIo9GI2bNn47HHHnOMopSUlGDIkCGO4AQApk2bhry8PJw8eRI/+MEP/GkSEamcq2mc1OQ41NY3uX1M20JpSo5uBNqbJeckBSfzx/bEzb1TQ7I5IZFa+Ryg2O12PPzwwxg3bhwGDx7suP3uu+9Gr169kJWVhePHj2P58uU4ffo03n33XQCA2Wx2Ck4AOK6bzWaXr9XY2IjGxkbHdavV6muziSiE3E3jeApO2lKyUFowSC2sptPpMGf4DQFuDZG2+Byg5Ofno6ysDAcPHnS6fcmSJY5/DxkyBJmZmZg8eTIqKirQt29fn16rsLAQq1ev9rWpRKQCvhYna0upQmnBIrWwGguwEXXkU2r40qVLsX37duzduxfdu3f3eOyYMWMAAGfPngUAGI1GVFdXOx0jXjcajS6fo6CgABaLxXG5cOGCL80mohDypTiZSOlCacEy39Qb3mZrWICNyDVZAYogCFi6dCm2bNmCPXv2IDs72+tjSktLAQCZma0JXyaTCSdOnEBNTY3jmJ07d0Kv1yMnx/U6//j4eOj1eqcLEWmLv9MzShZKC5a4mCgsnuD57yQLsBG5JmuKJz8/H5s2bcLWrVvRuXNnR86IwWBAYmIiKioqsGnTJsycORNpaWk4fvw4HnnkEUycOBFDhw4FAEydOhU5OTmYP38+nn/+eZjNZqxcuRL5+fmIj49XvodEpApSp2d0OqDt2kKxUJlWV7WIBdba10FhATYiz2QtM9bpXP96Wb9+Pe677z5cuHAB9957L8rKylBfX48ePXrgjjvuwMqVK51GPb766ivk5eVh3759SE5OxsKFC/Hss89KLtTGZcZE2mOzCxj/3B6YLQ2y8lDEvzrBqoMSKG0ryfZKTcJ8U2+OnFDEkXP+9qsOSqgwQCHSJnEVDwDZQYoW66AQkTM552+G70QUNOJ+N0aD83RPanKsx8e1rYNCRJGBm98QUVC52u/GbG3AI/9T6vWxoayDEqpNDP2l1XYTMUAhoqBrv99NScVlSY8LVR2UUG9i6CuttpsI4BQPEanA6OxUZBoS4O53fSjroIh5M+1ruIibGBaXVQW9TVJotd1EIgYoRBRy0VE6rJrduty2fZAiXg9FHRRP1W/F21ZvK4dNzm6AQaDVdhO1xQCFiFTBXQKt0ZAQsiXG3qrfqjV5V6vtJmqLOShEpBquEmhDmdQpNSlXbZsYarXdRG0xQCEiVWmfQBsK4sqXM9VXJR2vtk0MpbZHbe0maosBChFFDClLbl2tfHFHLCCntk0MxaRjd1V71dpuorYYoBBRRJCy5FZc+SIldTSUybveiEnHeRuPQQfnqr1qbrdSWPslPLDUPRH5RQsnA2+BxyO5NyLvlr740Qt7JY2cANqoJxKJdVAisc9awr14iCgotHAyEDcp9BZ4dEmKxXfXmr0+39Jb+2Jcv26qDMRc0UIAqRR3gWi4bDgZDuScvznFQ9ROJP1B94e7k4FYCEwtJwNvS25FUoITAOif0TnkSbxyqCHpOBi81X7RobX2y5QcI/8/awQDFKI2tDAioAZaOhkovZSWK1/USU7tl0gI2MIBC7UR/QdLg0unpUJgSgUUSpXbt9kFlFRcxtbSb1BScZnVXBXC2i/hhyMoRNDWiIAaaOlk4G3JrRRKrXzhCF3gsPZL+OEIChG0NSKgBv6eDII1iiDmE80YbJQVnKQmxzldV6LcPkfoAkvNG06SbziCQgRtjQiogT+FwII1iuDqdaJ0gKdYSGz3/kdvxdGvvlMsUZojdIEX6bVfwhFHUIjA4WG5fN19OFijCO5ex1twArS2Oy4mCqa+aZgz/AaY+qb5fVLjCF1wqHHDSfIdR1CIwNLgvhBPBu1HKYxuRkOCNYrQ1GLHii0n3E7p6AAYkmKREBMNs9V7u5XAEbrgUduGk+Q7BihE4PCwr+ScDIKxDLS4rAortpShtt59TRMBwJVrzXhr0QhERemCchJT2whduNf6iZTaL+GOAQrRf8gdEaBWUk8GgR5FkLOPDgB8W9+IOcNv8Om15FLTCB1XEpFWMEAhaoPDw4ETyFEET9NHSr6Or4IxQid1p2YtVP8lAhigEHXA4eHACOQogtRy9v6+jj8COUInZVSEK4lIaxigEFFQ+DKKIDVXQu60UKjyiQIxQid1VISl4ElrGKAQUdDIGUWQkyshdbomNTkWz9wxJKTTGEqO0MkZFeFKItIaBihEFFRSRhHk5kpIKWeflhyHkoLJiIsJn/JPckZF1LaSiMib8PmfSkSaIY4iuCqE5m1UAGgdFWhbHt9b4TgdgKfvGBxWwQkgb2UUS8GT1oTX/1Yi0jxfq65GYhVROaMivlb/JQoVTvEQkar4kysRacvE5a6MYq0f0hIGKESkCuKKnTPVVyUd7270IJKWifuyMirSgjjSLgYoRBRyrlbsuMN9kZz5MioSSUEcaRcDFCINCcc9VOSUqFc6VyJc3k+OilA4YoBCpBHhuIeK3BL1SuZKhNv7yVERCjc6QRDkbF+hClarFQaDARaLBXq9PtTNIQo4d6MM4u9jra5SKam4jHnrDnk9bumtfTGuXzfFRgXC9f0kUjs5529Zy4wLCwsxatQodO7cGenp6bj99ttx+vRpp2MaGhqQn5+PtLQ0dOrUCXPnzkV1dbXTMefPn8esWbOQlJSE9PR0PProo2hpaZHTFKKI4UtdEK2QumKnf0bnDvVSfBXO7ydROJEVoOzfvx/5+fk4dOgQdu7ciebmZkydOhX19fWOYx555BFs27YN77zzDvbv34+LFy/izjvvdNxvs9kwa9YsNDU14ZNPPsHrr7+ODRs24PHHH1euV0RhxNe6IFoQiuqm4fx+EoUTWTkoxcXFTtc3bNiA9PR0HD16FBMnToTFYsFrr72GTZs2YdKkSQCA9evXY9CgQTh06BDGjh2Lf/3rXygvL8euXbuQkZGB4cOH46mnnsLy5cvxxBNPIC4uTrneEYWBcN5DZWSvLojSAZ4GK6J0rccpJZzfT6Jw4lclWYvFAgBITW1d7nf06FE0NzcjNzfXcczAgQPRs2dPlJSUAABKSkowZMgQZGRkOI6ZNm0arFYrTp486U9ziMJSOO+hcvSr7zwGJ0Br8HL0q+8Ue81wfj+JwonPq3jsdjsefvhhjBs3DoMHDwYAmM1mxMXFISUlxenYjIwMmM1mxzFtgxPxfvE+VxobG9HY2Oi4brVafW02kebIrRaqZja7gE/Ofov/PfY1rjW1IDFW2p8gJUczwun9JAqE6002PFNUjnOXr6F3WhJWzMxBYlx00Nvhc4CSn5+PsrIyHDx4UMn2uFRYWIjVq1cH/HWI1MiXaqFqVFxWhWX/+ALXmmyyH6vkaEa4vJ9EgbD4jU+xs7zGcf2jM8Cbh85jSk461i0YFdS2+DTFs3TpUmzfvh179+5F9+7dHbcbjUY0NTXhypUrTsdXV1fDaDQ6jmm/qke8Lh7TXkFBASwWi+Ny4cIFX5pNpFla3wivuKwKD2w8Jjs4CdQOu1p/P4kCoX1w0tbO8hosfuPToLZH1giKIAj4xS9+gS1btmDfvn3Izs52un/kyJGIjY3F7t27MXfuXADA6dOncf78eZhMJgCAyWTC008/jZqaGqSnpwMAdu7cCb1ej5ycHJevGx8fj/j4eNmdIwonWqkW2r4668heXbBqq/z8skCPZmjl/SQKhutNNrfBiWhneQ2uN9mCNt0jK0DJz8/Hpk2bsHXrVnTu3NmRM2IwGJCYmAiDwYBFixZh2bJlSE1NhV6vxy9+8QuYTCaMHTsWADB16lTk5ORg/vz5eP7552E2m7Fy5Urk5+czCCHyQu3VQl1VZ01NjkVtfbPs5wrGDrtqfz+JguWZonLJxz11+5AAt6aVrABlzZo1AIBbbrnF6fb169fjvvvuAwC8+OKLiIqKwty5c9HY2Ihp06bh1VdfdRwbHR2N7du3Iy8vDyaTCcnJyVi4cCGefPJJ/3pCRCHlrjqrnOBkak46Zg3N4mgGUZCdu3xN0eOUIHuKx5uEhAS88soreOWVV9we06tXLxQVFcl5aSJSMbl76rgzJjsNc4bfoEibiEi63mlJ+OiMtOOCxa86KEREgPfqrFJE6YD5pt7KNIiIZFkx03UOqK/HKYEBChH57eJ3/g/7Lp6QjbgY/kkiCoXEuGhMyUn3eMyUnPSg1kPhXwMi8kthUTl++c/jko5NcBGA6HTAzydmoyCIv8yIqKN1C0a5DVJCUQfF50JtRESFReX4y4FKr8eJ1Vn3P3orDv/fZUcl2VG907Dwh70VGTlpv7yZSbZE8q1bMEr7lWSJKLI1tdjxVwnBiWjV7BzExURhwo3dMOHGboq2xdXy5swgLFMmCkeJcdFBW0rsCad4iMgnr39SKWnVTqf4qIBWZxWXN7dP0jVbGpC38RiKy6oC8rpEFFgMUIjClM0uoKTiMraWfoOSisuweds2WObzvv/FRUnHm/p0C1hw4ml5s3jb6m3livWdiIKHUzxEYShQUx6untebTvGBm7v2trxZAFBlacCRylpWjCXSGI6gEIWZQE15bC+9iAdcPK83d47o7v0gH9XUSWuL1OO0LlCjZkShwBEUojDibcpDh9Ypjyk5RlkrXJ7+oBzrPpKeECtKjo/GD/t1lf04qdI7J3g/SMZxWsZEYQo3HEEhUgGlfvnKmfKQqrDIt+AEAGKjo7Cz3OzTY6UYnZ2KTEMC3IVaOrSepEdnpwasDWrARGEKRxxBIQoxJX/5Kj3l0dRi9zk4AQDLtWbkbTwWsFU80VE6rJqdg7yNx6ADnEaOxKBl1ewcx2hRONZKCdSoGVGocQSFKISU/uWr9JTHmyXn4E8aQzBW0kwfnIk1946A0eDcJ6MhwSkwKi6rwvjn9mDeukN4aHMp5q07hPHP7dH86EIgRs2I1IAjKEQhYLMLOPR/l/Gb/z2h6C9fccrDbGlw+bxiRVd3Ux7tRxjOXa6X2CP3grGSZvrgTEzJMbodHREDwfbviRgIBrJOS6AxUZjCFQMUoiCTulTXlxO73CkPb+3qnKDcn4hAnyCjo3Qu36dwnwJhojCFK07xEAWRuykdT+Se2KVOeUhpV11Di6zX9iRUJ8hwnwJhojCFK46gEAWJp1/ynvhyYvc25aFEu+QI5Qky3KdA/Bk1I1IzBihEQeLtl3x73vJFvHE35eFru3Q6QPAhitEhtCfIYE+BhGKlkDhq1n6KzqihOijhuMKK/MMAhShI5PxCD+YvX6nt+v1PhuHKtSacu3wNgIDh3VPw9ZUGvLTr3wDgcgSmS1IsCu8cEtITpL+Jw3KEslianFEztWGROXKFAQpRkMj5hR7IX77tf6l2TY6X9LislETMHdmxbP0AY6cOJ5eUxFjcP643lk7qH/ITZLCmQNSwUkjqqJmaqOF9I3XSCYIvg7ahZbVaYTAYYLFYoNfrQ90cIklsdgHjn9vj9pc8AKQkxeKVeSMwtm9aQE7srn6pGvUJaGixwXKt2eMIw8Hlk9y2SQvD84H8lS5+tu6myqS8h5GI71vkkXP+5ggKUZBI+SX/7J1DMK5/YPaucfdLtdr6fcDk6wiDFn65B3IKhLsq+4bvG3nCAIUoiEKVzCilFkhKUiziY6JgtjYGrV3BFqhAKtxXCgUK3zfyhAEKUZCFIplRyi/V7641463/HoMonU7VUzVqxGJpvuH7Rp4wQCEKgWBPiUj9Bfrt1UbMGX5DwNqhhVwVXwRzpVA4kfq+jezVBSUVl8Pue0OeMUAhCjOuggA1/FIN56WkLJbmGynv24+HZeJHL+wNy+8NecZVPERhxF0Q8NisQXjqg1Nef6kGarWEuwRd8ZXCZSlpOAdhgeTuffvxsEz89UBl2H9vIomc8zcDFKIwUXT8Ih7c9HmH28U/5ksmZuOvByoBuP6lGqg/9pG2lDRcp7ECrf37NrJXlw4jJ22F2/cmUnCZMVGEKTpehaVvdwxOgO9X6bz/RRVeuXsEnvoguCuIIm0pqRaWXKtR+/etpOJyRH1vqCMGKEQa4urX+c5yMx7cdMzj48Q/5l2S43Bw+aSg/sLnUlLyBb83xACFSCNcV4GNR0OLXfJz1NQ1BP0XvhoSdEl7+L2hqFA3gIi8E5NM2w95m62NuHKtWfLzhOKPubiU1N0YjQ6tCZFcgktt8XtDDFCIVM5TFVg5QvXHXFxKCqDDyYZLcMkdfm+IAQqRj2x2ASUVl7G19BuUVFyGzR6YBXHekkylCuUfc7HEv9HgPIJjNCRwqSi5xe9NZGMOCpEPglnvwt8kwCgd8Od5Pwj5H/NQlPgn7eP3JnLJHkE5cOAAZs+ejaysLOh0Orz33ntO9993333Q6XROl+nTpzsdU1tbi3vuuQd6vR4pKSlYtGgRrl696ldHiILFbT6IpQF5G4+huKxK0dfzN2/kz/NGYObQLADBG/VxR0zQnTP8Bpj6pvEkQ5LwexOZZI+g1NfXY9iwYfjZz36GO++80+Ux06dPx/r16x3X4+Pjne6/5557UFVVhZ07d6K5uRn3338/lixZgk2bNsltDlFQSdkVePW2ckzJMSr2R3TPl9U+Pa79iA6rnBKRlsgOUGbMmIEZM2Z4PCY+Ph5Go9HlfadOnUJxcTE+/fRT3HzzzQCAP/3pT5g5cyZ+//vfIysrS26TSEO0XmVTatGxDR9XomvneL/7WFhUjnUfVcp+3PyxPfHEjwc7XtddqXlx1Ifz+USkNgHJQdm3bx/S09PRpUsXTJo0Cb/73e+QltZad6GkpAQpKSmO4AQAcnNzERUVhcOHD+OOO+4IRJNIBcLhF7zUfJCnPjjl+LevfWxqsfsUnETpgMduu8kRnDS12LFiy4mgjvoQEflL8VU806dPxxtvvIHdu3fjueeew/79+zFjxgzYbDYAgNlsRnp6utNjYmJikJqaCrPZ7PI5GxsbYbVanS6kLcHO2wgUX/JBfO3jmyXn4EuKyOIJ2YiLaf2vXVxWhbGFu1Fb775WStuS4UREaqH4CMpdd93l+PeQIUMwdOhQ9O3bF/v27cPkyZN9es7CwkKsXr1aqSZSkIUibyNQxOJR7nYFdsXXPn5Ve01W23Ro3RCwYGZr7Qh30zruhLpkuNan/4hIWQFfZtynTx907doVZ8+exeTJk2E0GlFTU+N0TEtLC2pra93mrRQUFGDZsmWO61arFT169Ahou0k54bRZnFg8Km/jMegAWUGKpz42tdjxZsk5fFV7Db1SkzDf1Bu9UpMkt8vUpwte/9lYx8iJL8XdQlkyvOj4RazcWuY00qO16T8iUlbAA5Svv/4aly9fRmZm6x8Zk8mEK1eu4OjRoxg5ciQAYM+ePbDb7RgzZozL54iPj++wEoi0I9w2/RKLR7XPp5HCVR/FRNi20zlPF53C/eN6I0oHj9M8OgAnnpiGTgnO/5XlFHcTt60PVcnwwqJy/OVAx1ybKibwEkU02QHK1atXcfbsWcf1yspKlJaWIjU1FampqVi9ejXmzp0Lo9GIiooK/PrXv0a/fv0wbdo0AMCgQYMwffp0LF68GGvXrkVzczOWLl2Ku+66iyt4wpSWN/1qP+0wslcXHP3qOzS22PH7nwwDdMC3VxvxbV2jU2KsO+376O7kbBeA1w6ew9Duehz/2n3O1ZKJ2R2CE0B+sBeqKrNFx6tc9l8kQDvTf0SkLNkBymeffYZbb73VcV2celm4cCHWrFmD48eP4/XXX8eVK1eQlZWFqVOn4qmnnnIaAXnrrbewdOlSTJ48GVFRUZg7dy7++Mc/KtAdUiNveRuh/gXvjqtVR+1HNMRpiPvGZeNvBytl9VHKKp2yb6z473G98fdPnBNmo3StybBivkl7UoO91ORYPHPHkJCMUNjsAlZuLfN6nFam/4hIWbIDlFtuuQWC4H7M+cMPP/T6HKmpqSzKFkE85W2oddMvdwmm7adb2tYRkdtHKat07AKQmZKIL5+a0SFHRcw3cUVKMm9achxKCiZ7fJ5AOlJZi9r6JknHamX6j4iUw80CKSiU3PTrepMNj713AvNfO4zH3juB6002RdsqJ8FUPEachpDTR6mrdL6qvYa4mCgsmtAHT84ZjEUT+ngNKrztBKsD8PQdg0MWnADygg41Tv8RUWBxs0AKGiU2/Vr8xqfYWf79KrCPzgBvHjqPKTnpWLdglCLtlLt7cNsVOnL6KHWVjpzVPG25S+Y1qmR1jJxpKLVN/xFR4DFAoQ4CWY+iqcWOohMXce7yNfROS8LwHilIjIuW9Nj2wUlbO8trsPiNTxUJUnydThAfJ25s1par93S+qTeeLjrldZono7PvK9jUvBOsOA3lLRj83ZzBqmgvEQUXAxRyEshy9P6MflxvsrkNTkQ7y2twvckmOeBxx9fpBHeP8/SeLhqf7TVR9ukdX2LG0CyfT9KuAiY1aJub5C5G+/nEbMdOzEQUWZiDQg6BLEcvZfTDk2eKyiW9jtTjPBF/2UsNB3RoDThcTUN4e087xXv/jeBPGXqbXUBJxWVsLf0GJRWXYfOldn4AidNQme3ydtKS4/Dq3SPcrlIiovDHERQCENhy9EqMfpy7LC2hVOpxnsipFutpFZKU93T9x+cktcmXaSetbM6o5mkoIgodjqAQAHnl6OVSYvSjd5q0RFGpx3njbtVR+3Omp1VIUt7TK9fdb+LXltxpJ61tzihOQ80ZfgNMfdMYnBARR1DaiuTNygJZjl6J0Y8VM3Pw5qHzXp9jhYJTAq5+2YuVZKV8R6S+VymJsbBcb1asiF04bc5IRJGLAcp/aGU4PFACWY6+d1oSPjoj7Th3EuOiMSUn3eNU0ZScdL8TZNtzlWAqdbM/qe/V/eOy8dKufytWxC6cNmckosjFAAXuq4aag7hZWahHbwJZjl6p0Y91C0a5TbZVsg6KXE9tP4nXDp5zuu3polNYND5b0nu6dFI/DDB2klyvxNt3Jdw2ZySiyBTxAYoahsPVMHoTyHL0So5+rFswCtebbHimqNxRS2XFzBzFR06k+vGfP3K5mZ9dANZ9VIkpOekwWxq8vqdSE0WlfFe0vDkjEZFIJ3jaWEelrFYrDAYDLBYL9Hq9X89VUnEZ89Yd8nrc24vHBmQ43N3ojXhaCvZW88GsgyIK5eiHP363rRx/+9hzDROdDvjTT4fj6R1f+v2eSv2u2OwCxj+3x+vIzcHlk5iDQkRBJef8HfEjKKEcDlfD6E17gVzyqbbRD380tdjxmpfgBAAEAThUWYtfTx+I2quNSE2Og9GQKPs9lftd0drmjERE7UV8gBLo4XBP+QJqTWaUWqrd1+mep24folRTQ+bNknOSNhMEgI2Hz2Pj4dYcHHHkRO57J/e7ovZ9eIiIvIn4ACWQyaHepku0ksyohhwZtZG6E3F7viZe+/JdYQE0ItKyiC/U5m1besC34XAphbK0kMyotYJfweLrDsNiELx6W7mssvO+fldYAI2ItCriAxTAfdVQT1VCPfGWLwC0nqBG9uricc8XT3u8BIPUfqhtf5dgmG/q3aGqrFS+VOX1tj9QqL8rRERKi/gpHpGSw+FS8wWOfvWdqpMZg5UjE+oaMN64al9cTBRmDsnE9uO+jyDJmbpj4isRRRoGKG0otS29nHyBOcNvUG0yYzByZIKZ3yInEBKP3VluxnulF1Fb39ShfVNyMvwKUL6ta4TNLkgOKpj4SkSRhAFKAMjNF1BrMmOgc2SCWcFXTiDk6lhX7Xs4t79fbXrqg1P428FKWcGFWr8rRERKi/hCbYEQLoWyAtkP8bndBQFKvkdyiuEVHa/Cg5uOeX1OHYAMfTwAHaqtrt8fKUJVkI+IKBTknL+ZJBsAgVoZFGyB7Iec/BZ/yEn0LTp+EUvf9h6ciI81Wxsxb3RPAO7fn5SkWK/P07YNRETUigFKgCi9MihUAtWPYOS32OwCNnxcKSkQ+vOeM3hw0+eQGyP07prk9v1Ze+8IHF05BY/NGuTxOZQKxoiIwglzUAIoXPIFAtEPpfJb3CW+essjaW/9x+ckHeeqfaa+aR7fn66d4yU9V6gL8hERqQkDlABTamVQqCndDyUq+LpLfP3xsEz89UClrLyQK9ebZRzdsX2e3h8tFOQjIlIbTvFQwNnsAkoqLmNr6TcoqbjsWFrrT36Luwq3VZYG/EVmcJIQK++/gdz8GxZZIyKSjyMo5JU/hdS8Le/1pa6Hp8RXXzQ022UdL7fuCIusERHJx2XG5JE/hdSkLu/1FAC5uu9IZS3mrTukYC+luXVANyyZ2Nfn/BtuukhEkU7O+ZsBCnlMNJVaP8TVc/pb58TdCX3GYCP+7mNSqz908L9eidrL+hMRBZKc8zeneCKcuyDgsVmD8NQHp9zWD9GhtXbHlByjyxOsv/v4eKoyG4rgROSpz1KES9I0EVGgMUk2grlLNDVbGvDgps/9KqTmT50Tb8XVdIDPOwn7g/VKiIiChwFKhJJSYVUKd4GIP0trpYy+iAXVXK0A0gGYkpMesCCG9UqIiAKPAUqE8hYESOUuEPFnaa3UAGDRuN4uK7j+94Rs7CyvkV0VVqquneI7LJsmIiJlMQclQvk7CuCtkJq4tPaBja73thHgfmmt1NGX3BwjVszKcUo6vVzXgF/8T6nEXnSUGBuF6x6WHXdJisUv/1EKs7XRcRtX4hARKY8jKBFKTtXSYG94KGf0RUw6nTP8BliuN2Hp5lL4ui6tV1oi+md08njMd9eanYIToDVnJ2/jMRSXVfn0uq4K2RERRTrZAcqBAwcwe/ZsZGVlQafT4b333nO6XxAEPP7448jMzERiYiJyc3Nx5swZp2Nqa2txzz33QK/XIyUlBYsWLcLVq1f96gjJIzUIePVu3zYKFHNc3BFXAbk6GftSZfZ6kw2/eqfU7et5M3lgNxQ/9CMc/9oq+7H+7EhcXFaF8c/twbx1h/DQ5lLMW3cI45/b43OwQ0QULmQHKPX19Rg2bBheeeUVl/c///zz+OMf/4i1a9fi8OHDSE5OxrRp09DQ8P2Uwj333IOTJ09i586d2L59Ow4cOIAlS5b43guSTWoQMHNoJg4un4S3F4/Fy3cNx9uLx+Lg8klepzPkLDN2Rc4uyoVF5chZVYyrjfIqwgJAfEwUyp6YhtfuG41nitwHVN74ssLH0yoqf0ZkiIjCgewclBkzZmDGjBku7xMEAS+99BJWrlyJOXPmAADeeOMNZGRk4L333sNdd92FU6dOobi4GJ9++iluvvlmAMCf/vQnzJw5E7///e+RlZXlR3dIDqml5n2p3eHPMuO27fO0S7DNLuDhzcew7bhZVttEOgAv3zUcnRJa/xucu3zNp+dpS2q/pSyl9lRzhQXfiCjcKZokW1lZCbPZjNzcXMdtBoMBY8aMQUlJCe666y6UlJQgJSXFEZwAQG5uLqKionD48GHccccdHZ63sbERjY3fz/tbrfKH4ck1b0GAr5TawdddcFR0vAor3ytD7bUmn9rnKrG1d1oSPjrj4UESSOm3zS5gw8eVPheyY8l8IooEigYoZnPrL9mMjAyn2zMyMhz3mc1mpKenOzciJgapqamOY9orLCzE6tWrlWwqtRGI6qZijovZ0uBylMDbKiBPCovK8ZcDlT61KykuGusW3IyxfdI6BGErZubgzUPnfXpeqf1xFVx40n5ExlOF3byNx/wuxU9EpBaaWMVTUFAAi8XiuFy4cCHUTaJ22q9EASA70VWK949943NwAgB/+K9hGNevq8vXTYyLxpScdBePcuZrf9zlnHjSdkRGSnE9XxJ1iYjUSNEAxWg0AgCqq6udbq+urnbcZzQaUVNT43R/S0sLamtrHce0Fx8fD71e73Qh9XC3EgWA5ERXKZ7+oBz//z9KfWpjl6RYrJXwmusWjHIbpEzJScdaH/vjKbhwxVUhO38Tj4mItETRKZ7s7GwYjUbs3r0bw4cPB9CaL3L48GHk5eUBAEwmE65cuYKjR49i5MiRAIA9e/bAbrdjzJgxSjaHgkDKlMPB5ZP8znEpLCrHuo/kj5zoAGy4bxTG39hN8muuWzAK15tseKaoHOcuX0PvtCSsmJmDxLhoAPApZ0dO5V53IzJKJB4TEWmF7ADl6tWrOHv2rON6ZWUlSktLkZqaip49e+Lhhx/G7373O/Tv3x/Z2dl47LHHkJWVhdtvvx0AMGjQIEyfPh2LFy/G2rVr0dzcjKVLl+Kuu+7iCh6NkbMSxZ8cl+tNNvzVx2mdJROz8aOB3qdt2kuMi8ZTtw9xeV8gVzUBHVdRiZRKPCYi0gLZAcpnn32GW2+91XF92bJlAICFCxdiw4YN+PWvf436+nosWbIEV65cwfjx41FcXIyEhO//aL711ltYunQpJk+ejKioKMydOxd//OMfFegOBZOcKQdfA5Tisio8+s/jsjYwBFqDoyUTs1EwM8en11Wa1KDhsVmDcN+4bJcjMoFMPCYiUhudIPhaGDx0rFYrDAYDLBYL81FCaGvpN3hoc6nX45be2g/9MzrJnt5xN33kzdwRN6DwzqGIi1FPDrjNLmD8c3u8BhcHl0+SlGgLOO86LT6Cq3iISM3knL+5WSD5TOqowJ/3fj8lKLVeh9ykUtHiCdmYNDADO8qq/KrponQhNLFyb97GY9DBdXAhZVWT1OJ6weIpV4eIyB8cQWlDTdU51dQWd7yNCrgi9Zd+ScVlzFt3SFZ77h/XC8Vl1X4XMAtkITSlnlsN34/Fb3yKneU1HW6fkpOOdQtGBbUtRKQNcs7fDFD+Q03VOdXUFm/cTTl4ImU6Q+r0kSh3UDp2n6rp0Aa5Ux/uppWUnEJRQ3DhL3fBiYhBChG5Iuf8rZ5J+hAK1aZt7Yub2exCwNri6rWU4G5TP0+k1OuQOn2kQ+u0zsmLVo8FzH67pQxbjn3tse/BKoQmrgKaM/wGmPp2rGirdtebbB6DEwDYWV6D6022ILWIiMJRxOeg+Ltpm69cjZIY9fFoaLEr3pZAj8i038/nTHUd/ry3wuvjauoa3I4meFuxAgD6hBgcXpGL0gtXPNZIEQBcrm/CI//4AoD7vgdjVVI4kLrr8zNF5W6XahMReRPxIyihqM7pdpTE2ogr15oVbYu716qyNOCBjcfw8q4zioymtB0VGNevm6THnPu23mUF2uKyKkdSKeC6tLwOwPM/GYrEuGjZhcncjUaxEJo0Und9VmJ3aCKKXBEfoAT7pOTr6hRf2iLltV7c9W+Me3Y3io5fVGwKSBz9cDfGo0Nr6fkXd53xOJXlbvqofWl5uYXJ3E3XsBCaNL3TkhQ9jojIlYif4gn2SUlOyXNvbfGWbCn1tczWRjy46XOn2/yZAvK0pBb/ud7YYnf52PZTWe2nj7p2igcE4Nv6RpRUXMbo7FRJ00GuXqf9dA0LoUkjddfnFSopkkdE2hTxAUqwT0r+jMS0bYuUvBJ/XqvtXjri88lZfeKuXkeUDrALwDUPCZTtgwdx+qi4rAq/eucLl31+bFYOHtx0THY/275HStUqCXfirs/eVvGwHgoR+SPip3i85ToAyp6UfB2JaduWneVmSSt9/Bn1aT8N4m7HYk+riqYPzsTB5ZPw5v2jMbp3FwCtwYlUbYMHT6ubHth4DCveOyH9idto/x5JnVaKdN52feYSYyLyF+ug/EegV7qIow9my3U89cEpfFff5HbExpAUi4SYaJitHdsyJceI8c/tcTt107bGCADZhdRceSS3P17adcan2iDiLsS+pLS8vXgsTH3THAXh/J0aa8tbLZZwqFUSDKwkS0RysNS9D9rnOih5UnIV/LgivtKzdw5x25aSisuylsKKUxb+WP/xOdlLn212AQ9vPoZtx82yX6/9tJq/eTu+TNf4smNxJPK06zMRkT8YoLQRiJOSnA3v2u+n4qotclcdiVMWT7xf7jQiI8eV69KWPovtLS6r8vn1XAUPvrZb1CU5DrX1TY7rodq3hoiIpGOAEkBSlvmmJsfisdtuglEvbcTGl1VH4ujQn/ecwYu7zkh6PNAaLCTHR+Nqo/eKoGJA5OsOxKL2wUNxWRWe2n7Sx2dr9disQTAaEh2jUSN7dcHRr77D1tJvOH1DRKRSDFACSMrURG19M4z6BMkjN76uOoqO0uGh3BsxwNhZ0nQT0Do6IiU4AYCuyfH4+My3+M3/nvA5OPntzIH42fg+jmDB32BHZDQkOo3u/OiFvZrY54iIKJJF/CqeQApEETi5q47a78EzJceI/Y/eitTkWMmv6YkOQEpSLH75zhe457XDHqeDvLFcb3H8W4mCdjq0Bh9isBaqPZeIiEg+jqAEUKCKwLmrMeJqesTVyqS7RvVAbb3vgYRITD5tLc/v//P9ee9Z/O+xr7Fqdg4MiXGSRnk6xcfgamOL10TYUO25REREvmGAEkCBLALnbdWRu+kRs6VBVh6KJ/rEGKdRDyWIoxk/G9db0vFPzbkJiXHRXoM1bgRIRKQtDFACKNCVSd2tOvI2WqCEGYON2FEmfwmxN+JoxpbSbyQdL+aXeFsizo0AiYi0hTkoARaKyqRK7PfjTcnZbwP23AJak4dTk+M8bjjYNr+k7W7KYnn8trgRIBGRtnAEJQgCWQTOFTmjAK428/N2fHxMFK40KDu148rtw7Ow/uNziow+cSNAIiJt4QhKkHj7hS9F+xU5Njf146WOAvxkRHcYEp1X82QaEvDzidnQoeMqIaA1UGhwsxOx0qbkGBUbfQr2nktEROQfjqBohJy9gryNFgCtuwr/89jXjuspibG4f1w2lk7qh+goHX7Qs4vkeilKazuaER2lU2z0SerqJyIiCj1uFqgB7lbkeNqsT3wMIG0Kx9Vz2ewCPjnzLX71v1+g2troc/vlkLIBob+4ESARUWjIOX9zikflpKzIWb2tvMN0j7vkXHfnYVfP9XzxKcxff8Tn4CQ+pvXr5WpKRQfg5xOzkRnE5GGREtNtREQUWJziUTl/6ne0T879tq4RT31wStJz7TpVjdcOVvrc7tuGZuLlu36AneVmj1Mqv54+KOxHMzhiQ0QkHwMUlZNTv8PdiVAMXLZKrC2ydu+/sf9srU/t1QE48cQ0dEpo/Wp5W8EUiB2k5Qh08CAnd4iIiL7HAEVhSp/wpK7IOfftNYx/bo/HE6HU5/I1OAGA/56Q7QhORO2DEHE1UqhHFAIdPHiq5pu38VjAp7KIiLSMSbIKCsQJz2YXMP65PR7rdxiSYmG51uw1idbbcynBW3/9fY+UCgB9STyWQ3yv3U3PiSuVDi6fxOkeIooYcs7fDFAU4usJT8oJ19uKnOT4aNQ32ty2LTU5Fo/ddhOM+gR8V9+E/E3SV/fIJWVlka9BgVIBYDCCh5KKy5i37pDX495ePJZ7/xBRxOAqniDzdaVNcVkVxj+3B/PWHcJDm0sxb90hjH9uD4rLqpyOc7ciR+QpOAFay8Y/8j+tz//UB+VYMjEbhqRYj4/xlbv++voeicTgpn1QIU6XtH/PPJGTeOwr7v1DROQfBigK8OWEJ/eEO31wJg4un4RHcm/0q61mSwP+eqASgRw4c9Vff4ICf4Ob9oIRPHDvHyIi/zBAcUFqSXmR3BOe3BOu2J73S7/B6yXnJPbCNeE/F8t13/fSiYuWNu3R9n3xJyhQesQjGMGDWM1X6maHRETkjKt42vElz0HuCU/OCddyvSlkJeddmZbTDQt+2Af3/O2w12Pbvi/+BAVKj3gEY+NAce+fvI3HFNnskIgo0nAEpY2i4xfxgA95DnJ+LdvsAj4+e0lSe3aWm11OA4XK0O56/GXBaIztkyZ7dEB8jzxxN6Kg9IhHsDYOdJc7FIxquUREWqd4gPLEE09Ap9M5XQYOHOi4v6GhAfn5+UhLS0OnTp0wd+5cVFdXK90M2YqOV2Hp25+7vM9bnoPUE97OcjPGP7cHf95bIalN//js64AtB5Zr4Q974f2lEwD4doKPjtLhx8M8n5B/PCzTZVAQiOmSYAUPYu7Q24vH4uW7huPtxWNxcPkkBidERF4EZIrnpptuwq5du75/kZjvX+aRRx7BBx98gHfeeQcGgwFLly7FnXfeiY8//jgQTZGkuKwKD/5n6a07nkrKA953ygXgcomtJ1cbfc8TUdKiH/bCYz8e7HTblBwjHs69Ees/rsSV682O293tDGyzC3j/C88rbd7/ogq/nj6oQ5ASqOkSb1VulRLqarlERFoUkAAlJiYGRqOxw+0WiwWvvfYaNm3ahEmTJgEA1q9fj0GDBuHQoUMYO3ZsIJrjkZiwKpWnPAd3JzwAGP/cHtWMhsgxJSe9Q3DiKk8nJTEW94/rjaWT+rs8wXvLuwH8CwB9HZFg8EBEpE4BCVDOnDmDrKwsJCQkwGQyobCwED179sTRo0fR3NyM3Nxcx7EDBw5Ez549UVJS4jZAaWxsRGPj9zvqWq1Wxdoq5cTZlrc8B1cnvJKKy6rJI5EjJTEGa++92ek2d8XWLNeb8dKuMxhg7OwyWFAi0TVYIx5ERBR6iuegjBkzBhs2bEBxcTHWrFmDyspKTJgwAXV1dTCbzYiLi0NKSorTYzIyMmA2m90+Z2FhIQwGg+PSo0cPxdorp9aFr8tCtVqM68r1Fqelu/7UI1Eq0VUMAOcMvwGmvmkMTtyQu1SeiEhtFB9BmTFjhuPfQ4cOxZgxY9CrVy/84x//QGJiok/PWVBQgGXLljmuW61WxYIUObUufF3ZoeViXG2DKznLo9uPIgVjaS+14g7KRBQOAr7MOCUlBTfeeCPOnj0Lo9GIpqYmXLlyxemY6upqlzkrovj4eOj1eqeLUqQsf43SAa/e/QOf/7h7W4XiSWJstE+vqZS2wZU/0zTiKh5Pv+NZF8R/Sm4JQEQUSgEPUK5evYqKigpkZmZi5MiRiI2Nxe7dux33nz59GufPn4fJZAp0U1ySsvx10fhszBya5ddruFuW6831ZhtCcc52tXRX6kjQmeqrHaYVisuq8NcDlW4fs2RitqK/7iNxikPpLQGIiEJJ8QDlV7/6Ffbv349z587hk08+wR133IHo6GjMmzcPBoMBixYtwrJly7B3714cPXoU999/P0wmU0hW8ADSlr9uP17l9x91d3U3dBKCj1CdT9qPaEgdCfrz3rNOGx96OnGK3v/C//dYJHUTxnATjE0QiYiCRfEA5euvv8a8efMwYMAA/Nd//RfS0tJw6NAhdOvWDQDw4osv4rbbbsPcuXMxceJEGI1GvPvuu0o3QzI5y1/91bZo16JxvZGaHIsA7tnns0w3xcrkjgSJ0wp/3nM2aO9xJE9xcAdlIgoniifJbt682eP9CQkJeOWVV/DKK68o/dI+CfYf9egoHSzXm/D3j8+psi7KI7n93dYyAdzXI3FFQGsgs/4T91M7bfn7Hnub4tChdYpjSo4xLHNduIMyEYWTiN8sUIk/6ja7gCOVtTBbG1B7tRGpyXEwGhJd1uhoarFjxZYTqgtO5KzyaFuP5OOzlzyW7hcAXLnW7Pb+tvw9cfqzyigccKUUEYWTiA9Q/P2j7mpJp6j9Sb+4rAortpShtl7aCTuYbsrSy0pSFeuRSB31SEmMheV6c0BPnJE+xcEdlIkonET8bsb+7GzrLt9BVNUm70E8tra+ScHWK2fXqRosfuNT2Y+TOupx/7jeAAK7ezCnOLiDMhGFj4gfQQF82+dFysoUoPVX7BPvnwSgU920Tns7y2twvcmGxDjptVekjkAtndQfA4ydFd9Lx5e2hPsUB7cEIKJwoBMENa4j8cxqtcJgMMBisShatE3MJZHyR72k4jLmrTuk2Gu3pdMhZKt75o/tiaduHyLrMeLoEOB6WqHtL3c577Ev5LSFiIiCS875myMobcjZ2TZQeQw6AIaEGFy53hKQ5/fm3OVrsh8jZwQq0LsHB2rXYyIiCi4GKD4KRB5DanIsFpqy8eKufyv+3FL1Tkvy6XFqmlZQU1uIiMg3DFB85C3fQa605DiUFEzGiCf/pcCz+W7FzByfHxvo0RE51NQWIiKSL+JX8fiq7eoff+j+c3n6jsEY+/S/cLXJ5vdz+mpKTrqsBFkiIqJAYYDiBzHfwdtuyJ6Iyz+f/qActddDG5ysWzAqZK9PRETUFqd4/NQ230GsJHu5vgmv7nNfXVX02KxBuHtMLyx87RAufOdb0u3QG/Q4/o3Vp8eO65uKPt06YcXMHLcjJ4FedUNEROQKAxQFtM932Fr6jaTHHThzCU99cMrn1314cn88POVGFB2/iJVb5VWolTJi4qpKrpyS+ERERL5igBIAUlf47P/3tz6/RqYhAb+Y3B8AMHNoFqYNznSMdJz7th5vHzkPs7XR5WOlBid5G491SAAWdwVmPREiIgokBigBMDo7FUZ9vNsAwV86dCwN334UZ+mk/jhSWYuvv7uGf50043qzDdldkz1O54i87QoMAL/dUoZJAzMQF8M0JiIiUh4DlACIjtJh3uieeHHXGcWfu0tSLArvHOJ19OL7gCUN/9/NPWS9hrddgQHgcn0TxhbuwjN3eG8LERGRXPz5GyC9uyYr/pwzB2fgs5VTAh4QSK2SW1vf7NgMkYiISEkMUAIkEJVmBxj1QVlBI7ftq7eVw2bX3JZORESkYgxQAkSsNKukt4+cD0ogILZdSigkAKiyNOBIZW2gm0VERBGEAUqAREfpsGLmIEWf02xtDEog4EuV3EBtnkhERJGJAUqAFJdVYdX7JxV/3mAFAmKV3NTkOEnHB2JKi4iIIhcDlAAQa4jU1jcp/tzBDASmD87EoYLJSE2OdXuMDq01WUZnpwatXUREFP4YoCjsepMNj/7zuE87HCfHRrnN+whVIBAXE4Vn7hji2NSwfZuAjjVZiIiI/MUARUGFReXIWVWMuoYWnx5/1+ieANQXCIjTPcZ2Sb/iRoesg0JEREpjoTaFFBaV4y8HKv16jtwcI0Zlp3bY/8aogv1v2m6KyI0DiYgo0BigKKCpxY51H/kenOjQGoSIJ3y1BgLty+kTEREFCgMUBbxZcg6+lidxNX3DQICIiCIdAxQFfFV7zefHqmH6hoiISG0YoCigV2qSpOP0CTF49s4h6JIcr7rpGyIiIjVhgKKA+abeeLrolMdpHp0OOLwiF4lx0cFrGBERkUZxmbEC4mKisHhCtsdjlkzIZnBCREQkEUdQFFIws3XvmnUfVTqNpETpgMUTsh33ExERkXc6QRACvz2uwqxWKwwGAywWC/R6faib46SpxY43S87hq9pr6JWahPmm3oiL4UAVERGRnPM3R1AUFhcThUUT+oS6GQAAm11QZT0VIiIibxiguBAOoyDFZVUdKtJmckkzERFpBKd42rhkbcQtv9+D+ia70+1ayyMRd1Nu/8GKYyfcP4eIiEJBzvk7pMMCr7zyCnr37o2EhASMGTMGR44cCVlbhj7xIUY9s6tDcAIAdgH4y4FKFBaVh6Bl8tjsAlZvK3e5m7J42+pt5bD5WvqWiIgoCEIWoPzP//wPli1bhlWrVuHYsWMYNmwYpk2bhpqamqC3ZegTH8IqYQfidR9VoqmlYwCjJkcqa52mddoTAFRZGnCksjZ4jSIiIpIpZAHKH/7wByxevBj3338/cnJysHbtWiQlJeHvf/97UNtxydooKTgBWkdS3iw5F9gG+ammzn1w4stxREREoRCSAKWpqQlHjx5Fbm7u9w2JikJubi5KSko6HN/Y2Air1ep0Ucodrx6Udbw/++4EQ3rnBEWPIyIiCoWQBCjffvstbDYbMjIynG7PyMiA2WzucHxhYSEMBoPj0qNHD8XaUlvfLOt4qfvuhMro7FRkGhLgbjGxDq2reUZnpwazWURERLJoYu1sQUEBLBaL43LhwgXFnjs1OVbysVG61n131Cw6SodVs1tXG7UPUsTrq2bnsB4KERGpWkgClK5duyI6OhrV1dVOt1dXV8NoNHY4Pj4+Hnq93umilC0Pjpd87OIJ2ZqohzJ9cCbW3DsCRoPzNI7RkMAlxkREpAkhKdQWFxeHkSNHYvfu3bj99tsBAHa7Hbt378bSpUuD2pZu+njoE2K8Jsr+fKJ26qAArUHKlBwjK8kSEZEmhayS7LJly7Bw4ULcfPPNGD16NF566SXU19fj/vvvD3pbjj8xze1S4/gYHU48MV0TIyftRUfpYOqbFupmEBERyRayAOWnP/0pLl26hMcffxxmsxnDhw9HcXFxh8TZYDn+xDRcsjbijlcPora+GanJsdjy4Hh008eHpD1ERESRjKXuiYiIKCg0U+qeiIiIyBUGKERERKQ6DFCIiIhIdRigEBERkeowQCEiIiLVYYBCREREqsMAhYiIiFSHAQoRERGpDgMUIiIiUp2Qlbr3h1j81mq1hrglREREJJV43pZSxF6TAUpdXR0AoEePHiFuCREREclVV1cHg8Hg8RhN7sVjt9tx8eJFdO7cGTqdTtHntlqt6NGjBy5cuBB2+/yEc98A9k/r2D9tC+f+hXPfgOD2TxAE1NXVISsrC1FRnrNMNDmCEhUVhe7duwf0NfR6fVh+EYHw7hvA/mkd+6dt4dy/cO4bELz+eRs5ETFJloiIiFSHAQoRERGpDgOUduLj47Fq1SrEx8eHuimKC+e+Aeyf1rF/2hbO/QvnvgHq7Z8mk2SJiIgovHEEhYiIiFSHAQoRERGpDgMUIiIiUh0GKERERKQ6DFDaeOWVV9C7d28kJCRgzJgxOHLkSKib5JMnnngCOp3O6TJw4EDH/Q0NDcjPz0daWho6deqEuXPnorq6OoQt9uzAgQOYPXs2srKyoNPp8N577zndLwgCHn/8cWRmZiIxMRG5ubk4c+aM0zG1tbW45557oNfrkZKSgkWLFuHq1atB7IVr3vp23333dfgsp0+f7nSMWvsGAIWFhRg1ahQ6d+6M9PR03H777Th9+rTTMVK+j+fPn8esWbOQlJSE9PR0PProo2hpaQlmV1yS0r9bbrmlw2f4wAMPOB2jxv6tWbMGQ4cOdRTvMplM2LFjh+N+LX9ugPf+afVzc+fZZ5+FTqfDww8/7LhN9Z+hQIIgCMLmzZuFuLg44e9//7tw8uRJYfHixUJKSopQXV0d6qbJtmrVKuGmm24SqqqqHJdLly457n/ggQeEHj16CLt37xY+++wzYezYscIPf/jDELbYs6KiIuG3v/2t8O677woAhC1btjjd/+yzzwoGg0F47733hC+++EL48Y9/LGRnZwvXr193HDN9+nRh2LBhwqFDh4SPPvpI6NevnzBv3rwg96Qjb31buHChMH36dKfPsra21ukYtfZNEARh2rRpwvr164WysjKhtLRUmDlzptCzZ0/h6tWrjmO8fR9bWlqEwYMHC7m5ucLnn38uFBUVCV27dhUKCgpC0SUnUvr3ox/9SFi8eLHTZ2ixWBz3q7V/77//vvDBBx8I//73v4XTp08LK1asEGJjY4WysjJBELT9uQmC9/5p9XNz5ciRI0Lv3r2FoUOHCg899JDjdrV/hgxQ/mP06NFCfn6+47rNZhOysrKEwsLCELbKN6tWrRKGDRvm8r4rV64IsbGxwjvvvOO47dSpUwIAoaSkJEgt9F37k7jdbheMRqPwwgsvOG67cuWKEB8fL7z99tuCIAhCeXm5AED49NNPHcfs2LFD0Ol0wjfffBO0tnvjLkCZM2eO28dopW+impoaAYCwf/9+QRCkfR+LioqEqKgowWw2O45Zs2aNoNfrhcbGxuB2wIv2/ROE1hNd25NCe1rqX5cuXYS//e1vYfe5icT+CUL4fG51dXVC//79hZ07dzr1SQufIad4ADQ1NeHo0aPIzc113BYVFYXc3FyUlJSEsGW+O3PmDLKystCnTx/cc889OH/+PADg6NGjaG5udurrwIED0bNnT032tbKyEmaz2ak/BoMBY8aMcfSnpKQEKSkpuPnmmx3H5ObmIioqCocPHw56m+Xat28f0tPTMWDAAOTl5eHy5cuO+7TWN4vFAgBITU0FIO37WFJSgiFDhiAjI8NxzLRp02C1WnHy5Mkgtt679v0TvfXWW+jatSsGDx6MgoICXLt2zXGfFvpns9mwefNm1NfXw2Qyhd3n1r5/Iq1/bgCQn5+PWbNmOX1WgDb+72lys0Clffvtt7DZbE4fAgBkZGTgyy+/DFGrfDdmzBhs2LABAwYMQFVVFVavXo0JEyagrKwMZrMZcXFxSElJcXpMRkYGzGZzaBrsB7HNrj478T6z2Yz09HSn+2NiYpCamqr6Pk+fPh133nknsrOzUVFRgRUrVmDGjBkoKSlBdHS0pvpmt9vx8MMPY9y4cRg8eDAASPo+ms1ml5+veJ9auOofANx9993o1asXsrKycPz4cSxfvhynT5/Gu+++C0Dd/Ttx4gRMJhMaGhrQqVMnbNmyBTk5OSgtLQ2Lz81d/wBtf26izZs349ixY/j000873KeF/3sMUMLQjBkzHP8eOnQoxowZg169euEf//gHEhMTQ9gykuuuu+5y/HvIkCEYOnQo+vbti3379mHy5MkhbJl8+fn5KCsrw8GDB0PdlIBw178lS5Y4/j1kyBBkZmZi8uTJqKioQN++fYPdTFkGDBiA0tJSWCwW/POf/8TChQuxf//+UDdLMe76l5OTo+nPDQAuXLiAhx56CDt37kRCQkKom+MTTvEA6Nq1K6KjoztkL1dXV8NoNIaoVcpJSUnBjTfeiLNnz8JoNKKpqQlXrlxxOkarfRXb7OmzMxqNqKmpcbq/paUFtbW1mutznz590LVrV5w9exaAdvq2dOlSbN++HXv37kX37t0dt0v5PhqNRpefr3ifGrjrnytjxowBAKfPUK39i4uLQ79+/TBy5EgUFhZi2LBhePnll8Pmc3PXP1e09LkBrVM4NTU1GDFiBGJiYhATE4P9+/fjj3/8I2JiYpCRkaH6z5ABClq/pCNHjsTu3bsdt9ntduzevdtpPlKrrl69ioqKCmRmZmLkyJGIjY116uvp06dx/vx5TfY1OzsbRqPRqT9WqxWHDx929MdkMuHKlSs4evSo45g9e/bAbrc7/uhoxddff43Lly8jMzMTgPr7JggCli5dii1btmDPnj3Izs52ul/K99FkMuHEiRNOgdjOnTuh1+sdw/Gh4q1/rpSWlgKA02eo1v61Z7fb0djYqPnPzR2xf65o7XObPHkyTpw4gdLSUsfl5ptvxj333OP4t+o/w4Cn4WrE5s2bhfj4eGHDhg1CeXm5sGTJEiElJcUpe1krfvnLXwr79u0TKisrhY8//ljIzc0VunbtKtTU1AiC0Lq0rGfPnsKePXuEzz77TDCZTILJZApxq92rq6sTPv/8c+Hzzz8XAAh/+MMfhM8//1z46quvBEFoXWackpIibN26VTh+/LgwZ84cl8uMf/CDHwiHDx8WDh48KPTv318VS3E99a2urk741a9+JZSUlAiVlZXCrl27hBEjRgj9+/cXGhoaHM+h1r4JgiDk5eUJBoNB2Ldvn9NyzWvXrjmO8fZ9FJc6Tp06VSgtLRWKi4uFbt26qWI5p7f+nT17VnjyySeFzz77TKisrBS2bt0q9OnTR5g4caLjOdTav9/85jfC/v37hcrKSuH48ePCb37zG0Gn0wn/+te/BEHQ9ucmCJ77p+XPzZP2K5PU/hkyQGnjT3/6k9CzZ08hLi5OGD16tHDo0KFQN8knP/3pT4XMzEwhLi5OuOGGG4Sf/vSnwtmzZx33X79+XXjwwQeFLl26CElJScIdd9whVFVVhbDFnu3du1cA0OGycOFCQRBalxo/9thjQkZGhhAfHy9MnjxZOH36tNNzXL58WZg3b57QqVMnQa/XC/fff79QV1cXgt4489S3a9euCVOnThW6desmxMbGCr169RIWL17cIWhWa98EQXDZNwDC+vXrHcdI+T6eO3dOmDFjhpCYmCh07dpV+OUvfyk0NzcHuTcdeevf+fPnhYkTJwqpqalCfHy80K9fP+HRRx91qqchCOrs389+9jOhV69eQlxcnNCtWzdh8uTJjuBEELT9uQmC5/5p+XPzpH2AovbPUCcIghD4cRoiIiIi6ZiDQkRERKrDAIWIiIhUhwEKERERqQ4DFCIiIlIdBihERESkOgxQiIiISHUYoBAREZHqMEAhIiIi1WGAQkRERKrDAIWIiIhUhwEKERERqQ4DFCIiIlKd/wfrWlCdlHQmPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c3384e-73f1-4aac-bded-af87a8e41083",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c9d1d1-02e8-4b97-ac79-6b5dc2c78fe9",
   "metadata": {},
   "source": [
    "#### metrics with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f22164e6-92e5-46a7-b0ca-ae46c90b1fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 18.571662086713673\n",
      "MSE: 1388.8581213327993\n",
      "RMSE: 37.267386832628866\n"
     ]
    }
   ],
   "source": [
    "# with hyperparameter tuning\n",
    "from sklearn import metrics\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0f45981a-651b-4fe7-bf9e-bf91230afa33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 18.571662086713673\n",
      "MSE: 1388.8581213327993\n",
      "RMSE: 37.267386832628866\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fbd9f1-6836-4a04-b533-b0378da7a0cf",
   "metadata": {},
   "source": [
    "#### metrics without hyperparamter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0b9b6fe3-176b-4891-b454-ce42271f3cce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 20.616109405453432\n",
      "MSE: 1623.4047019429356\n",
      "RMSE: 40.2914966456067\n"
     ]
    }
   ],
   "source": [
    "# w/o hyperparameter tuning\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d239d14b-b2fe-410e-a526-000892908b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(regressor,X,Y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ca5cb22-e2d5-4398-9618-be43eb8428e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744970110899368"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "313d2bd6-1bdc-4526-bee4-75589333ba76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a36c259d-0856-400c-8c3b-d060f46912dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xg_random.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "31ac3a81-b528-4fa5-b009-847cb572ed5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score: 0.8109016919523627\n"
     ]
    }
   ],
   "source": [
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R² Score: {r2}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ca7e5c-f906-434a-94f0-796973ffcb2e",
   "metadata": {},
   "source": [
    "### Hyperparamter tuning with Grid Search CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6bfc831-1b42-40f7-8942-c43b2343c1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3dd83b3f-129e-4f47-9c6c-983ce07aaecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.7, 0.8, 0.9],\n",
       "                         &#x27;learning_rate&#x27;: [0.05, 0.07, 0.1],\n",
       "                         &#x27;max_depth&#x27;: [6, 7, 8],\n",
       "                         &#x27;n_estimators&#x27;: [500, 700, 900],\n",
       "                         &#x27;subsample&#x27;: [0.7, 0.8, 0.9]},\n",
       "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.7, 0.8, 0.9],\n",
       "                         &#x27;learning_rate&#x27;: [0.05, 0.07, 0.1],\n",
       "                         &#x27;max_depth&#x27;: [6, 7, 8],\n",
       "                         &#x27;n_estimators&#x27;: [500, 700, 900],\n",
       "                         &#x27;subsample&#x27;: [0.7, 0.8, 0.9]},\n",
       "             verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.7, 0.8, 0.9],\n",
       "                         'learning_rate': [0.05, 0.07, 0.1],\n",
       "                         'max_depth': [6, 7, 8],\n",
       "                         'n_estimators': [500, 700, 900],\n",
       "                         'subsample': [0.7, 0.8, 0.9]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [500, 700, 900],\n",
    "    'max_depth': [6, 7, 8],\n",
    "    'learning_rate': [0.05, 0.07, 0.1],\n",
    "    'subsample': [0.7, 0.8, 0.9],\n",
    "    'colsample_bytree': [0.7, 0.8, 0.9]\n",
    "}\n",
    "\n",
    "# Initialize the model\n",
    "xgb_model = xgb.XGBRegressor()\n",
    "\n",
    "# Grid Search CV\n",
    "grid_search_xgb = GridSearchCV(estimator=xgb_model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the grid search model\n",
    "grid_search_xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83097ac7-9e44-4ab9-93bc-c03a3cce9280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.9,\n",
       " 'learning_rate': 0.07,\n",
       " 'max_depth': 7,\n",
       " 'n_estimators': 500,\n",
       " 'subsample': 0.8}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_xgb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "65e2a47c-8545-4275-bec5-22c64bf062bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76271611162995"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_xgb.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af9bb178-5ae2-475b-b895-78f48db0e1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_search_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80f5458c-1623-4214-9f5c-2ecb41af1304",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'r2_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mr2_score\u001b[49m(y_test, y_pred)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'r2_score' is not defined"
     ]
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02f16db-2441-4015-8bd0-b42fc63a1f54",
   "metadata": {},
   "source": [
    "#### Evaluation of GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac3382b8-8c5b-4c61-b1d1-bcfd793b8a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 18.138251779042562\n",
      "MSE: 1331.1399307561292\n",
      "RMSE: 36.48479040307247\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(f\"MAE: {mae}\")\n",
    "print(f\"MSE: {mse}\")\n",
    "print(f\"RMSE: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17abf7bf-5ef6-449c-b085-b33d0f455f44",
   "metadata": {},
   "source": [
    "### Using GridSearchCV's best params to train the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c711f1c2-9db5-4721-8017-af43f2e9512b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de146931-771b-497e-b047-a505ab163d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"AQI_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ec78c17-b19f-4784-991a-904415f13539",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31671b24-001e-4285-bf84-a11474035a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,:-1] ## independent features\n",
    "Y=data.iloc[:,-1]  ## dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5f223b53-9b9a-415b-b218-275cf523da5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c22e6ba9-43c1-4837-ad87-a6f7503eb87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params={'colsample_bytree': 0.9,\n",
    " 'learning_rate': 0.07,\n",
    " 'max_depth': 7,\n",
    " 'n_estimators': 500,\n",
    " 'subsample': 0.8}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f45cc85-d8de-4f72-8852-dc341cf23c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2401a7da-f745-4932-af9e-6515f09bcdc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_best = XGBRegressor(**best_params, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b07d0be7-f188-49fc-8165-13d37bca53b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.07, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.07, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.9, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.07, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_best.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39aa15a2-ec13-4a8d-b55f-d59fcb998521",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "673d5bfc-59c3-4897-9efc-685805e99e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8360996166839492\n",
      "Mean Absolute Error: 16.948350169621328\n",
      "Mean Squared Error: 986.8644965207172\n",
      "Root Mean Squared Error: 31.414399509153718\n"
     ]
    }
   ],
   "source": [
    "y_pred = xgb_best.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'R^2: {r2}')\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'Root Mean Squared Error: {rmse}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "36e03b0d-7dcf-4e45-955c-8bd8af28bff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "692a74d4-b574-447f-8ea1-18818dd2010f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['xgb_best_model.joblib']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xgb_best, 'xgb_best_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6a6537-3d56-4484-839f-8fb6b6c94126",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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