ML_in_Java

This project is in progress (I created only a structure of this project and planned what is in project see below)

if you want you can contribute according to project described below by opening a pull request on the time of contributing also erdit contribute.md

This project is a collection of machine learning algorithms and utilities implemented in Java. It includes various models, evaluation metrics, and cross-validation techniques to help you build and evaluate machine learning models efficiently.

Overview

The project is organized into the following components:

• DataLoader: Utility class for loading and preprocessing data.
• MathUtils: Contains mathematical utility functions.
• LinearRegression: Implementation of linear regression.
• DecisionTree: Implementation of a decision tree classifier.
• GradientDescent: Gradient descent optimization algorithm.
• KMeansClustering: Implementation of the k-means clustering algorithm.
• CrossValidation: Cross-validation utilities for model evaluation.
• Metrics: Evaluation metrics for regression, classification, and clustering.

Features

• DataLoader:

  • loadCSV(String filePath): Loads data from a CSV file.

• MathUtils:

  • mean(double[] values): Calculates the mean of an array of doubles.
  • standardDeviation(double[] values): Calculates the standard deviation of an array of doubles.

• LinearRegression:

  • fit(double[][] X, double[] y): Fits the linear regression model.
  • predict(double[][] X): Predicts target values using the fitted model.

• DecisionTree:

  • fit(double[][] X, double[] y): Fits the decision tree classifier.
  • predict(double[][] X): Predicts target values using the fitted classifier.
  • prune(int maxDepth): Prunes the decision tree to the specified maximum depth.

• GradientDescent:

  • fit(double[][] X, double[] y): Fits the model using gradient descent.
  • predict(double[][] X): Predicts target values using the fitted model.
  • fitPolynomial(double[][] X, double[] y, int degree): Performs polynomial regression.

• KMeansClustering:

  • fit(double[][] X): Fits the k-means clustering model.
  • predict(double[][] X): Predicts cluster labels for the input features.
  • getCentroids(): Returns the centroids of the clusters.
  • calculateSSE(): Calculates the sum of squared errors for the clusters.

• CrossValidation:

  • kFoldCrossValidation(Model model, double[][] X, double[] y, int k): Performs k-fold cross-validation.
  • stratifiedKFoldCrossValidation(Model model, double[][] X, double[] y, int k): Performs stratified k-fold cross-validation.
  • leaveOneOutCrossValidation(Model model, double[][] X, double[] y): Performs leave-one-out cross-validation.
  • randomSplitCrossValidation(Model model, double[][] X, double[] y, double testSize): Performs random split cross-validation.
  • monteCarloCrossValidation(Model model, double[][] X, double[] y, int numSplits, double testSize): Performs Monte Carlo cross-validation.
  • bootstrapCrossValidation(Model model, double[][] X, double[] y, int numSamples): Performs bootstrap cross-validation.

• Metrics:

  • meanSquaredError(double[] yTrue, double[] yPred): Calculates mean squared error for regression problems.
  • rootMeanSquaredError(double[] yTrue, double[] yPred): Calculates root mean squared error for regression problems.
  • meanAbsoluteError(double[] yTrue, double[] yPred): Calculates mean absolute error for regression problems.
  • rSquared(double[] yTrue, double[] yPred): Calculates R-squared for regression problems.
  • accuracy(double[] yTrue, double[] yPred): Calculates accuracy for classification problems.
  • precision(double[] yTrue, double[] yPred): Calculates precision for classification problems.
  • recall(double[] yTrue, double[] yPred): Calculates recall for classification problems.
  • f1Score(double[] yTrue, double[] yPred): Calculates F1 score for classification problems.
  • confusionMatrix(double[] yTrue, double[] yPred): Calculates the confusion matrix for classification problems.
  • aucRoc(double[] yTrue, double[] yPred): Calculates the AUC-ROC score for binary classification problems.
  • matthewsCorrelationCoefficient(double[] yTrue, double[] yPred): Calculates the Matthews correlation coefficient for binary classification problems.
  • precisionRecallF1MultiClass(double[] yTrue, double[] yPred): Calculates precision, recall, and F1 score for each class in a multi-class classification problem.
  • adjustedRandIndex(double[] labelsTrue, double[] labelsPred): Computes the Adjusted Rand Index for clustering evaluation.
  • silhouetteScore(double[][] X, double[] labels): Computes the Silhouette Score for clustering evaluation.
  • homogeneityScore(double[] labelsTrue, double[] labelsPred): Computes the Homogeneity score for clustering evaluation.
  • completenessScore(double[] labelsTrue, double[] labelsPred): Computes the Completeness score for clustering evaluation.
  • vMeasureScore(double[] labelsTrue, double[] labelsPred): Computes the V-Measure score for clustering evaluation.
  • mutualInformationScore(double[] labelsTrue, double[] labelsPred): Computes the Mutual Information score for clustering evaluation.
  • normalizedMutualInformationScore(double[] labelsTrue, double[] labelsPred): Computes the Normalized Mutual Information score for clustering evaluation.
  • randIndex(double[] labelsTrue, double[] labelsPred): Computes the Rand Index for clustering evaluation.
  • fowlkesMallowsIndex(double[] labelsTrue, double[] labelsPred): Computes the Fowlkes-Mallows Index for clustering evaluation.
  • jaccardIndex(double[] labelsTrue, double[] labelsPred): Computes the Jaccard Index for clustering evaluation.
  • meanSquaredLogarithmicError(double[] yTrue, double[] yPred): Computes the Mean Squared Logarithmic Error for regression problems.
  • explainedVariance(double[] yTrue, double[] yPred): Computes the Explained Variance Score for regression problems.
  • medianAbsoluteError(double[] yTrue, double[] yPred): Computes the Median Absolute Error for regression problems.
  • hammingLoss(double[] yTrue, double[] yPred): Computes the Hamming Loss for classification problems.

Usage

To use these classes and methods, you can import the respective classes into your Java code and create instances of the models or call the utility methods as needed.