diff --git a/scikitplot/__init__.py b/scikitplot/__init__.py
index 82ef1e5..e223419 100644
--- a/scikitplot/__init__.py
+++ b/scikitplot/__init__.py
@@ -1,5 +1,5 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-__version__ = '0.2.6'
+__version__ = '0.2.7'
 
 
 from scikitplot.classifiers import classifier_factory
diff --git a/scikitplot/classifiers.py b/scikitplot/classifiers.py
index d5fb36b..eba688e 100644
--- a/scikitplot/classifiers.py
+++ b/scikitplot/classifiers.py
@@ -53,8 +53,8 @@ def classifier_factory(clf):
     return clf
 
 
-def plot_confusion_matrix(clf, X, y, labels=None, title=None, normalize=False, do_cv=True, cv=None,
-                          shuffle=True, random_state=None, ax=None, figsize=None, 
+def plot_confusion_matrix(clf, X, y, labels=None, title=None, normalize=False, x_tick_rotation=0,
+                          do_cv=True, cv=None, shuffle=True, random_state=None, ax=None, figsize=None,
                           title_fontsize="large", text_fontsize="medium"):
     """Generates the confusion matrix for a given classifier and dataset.
 
@@ -79,6 +79,9 @@ def plot_confusion_matrix(clf, X, y, labels=None, title=None, normalize=False, d
         normalize (bool, optional): If True, normalizes the confusion matrix before plotting.
             Defaults to False.
 
+        x_tick_rotation (int, optional): Rotates x-axis tick labels by the specified angle. This is
+            useful in cases where there are numerous categories and the labels overlap each other.
+
         do_cv (bool, optional): If True, the classifier is cross-validated on the dataset using the
             cross-validation strategy in `cv` to generate the confusion matrix. If False, the
             confusion matrix is generated without training or cross-validating the classifier.
@@ -158,7 +161,8 @@ def plot_confusion_matrix(clf, X, y, labels=None, title=None, normalize=False, d
         y_true = np.concatenate(trues_list)
 
     ax = plotters.plot_confusion_matrix(y_true=y_true, y_pred=y_pred, labels=labels,
-                                        title=title, normalize=normalize, ax=ax, figsize=figsize, 
+                                        title=title, normalize=normalize,
+                                        x_tick_rotation=x_tick_rotation, ax=ax, figsize=figsize,
                                         title_fontsize=title_fontsize, text_fontsize=text_fontsize)
 
     return ax
diff --git a/scikitplot/helpers.py b/scikitplot/helpers.py
index 1f213ad..e04d260 100644
--- a/scikitplot/helpers.py
+++ b/scikitplot/helpers.py
@@ -42,7 +42,7 @@ def binary_ks_curve(y_true, y_probas):
                          '{} category/ies'.format(len(lb.classes_)))
     idx = encoded_labels == 0
     data1 = np.sort(y_probas[idx])
-    data2 = np.sort(y_probas[-idx])
+    data2 = np.sort(y_probas[np.logical_not(idx)])
 
     ctr1, ctr2 = 0, 0
     thresholds, pct1, pct2 = [], [], []
diff --git a/scikitplot/plotters.py b/scikitplot/plotters.py
index 8ba096b..699bed6 100644
--- a/scikitplot/plotters.py
+++ b/scikitplot/plotters.py
@@ -22,8 +22,8 @@
 from sklearn.metrics import silhouette_samples
 
 
-def plot_confusion_matrix(y_true, y_pred, labels=None, title=None, normalize=False, ax=None,
-                          figsize=None, title_fontsize="large", text_fontsize="medium"):
+def plot_confusion_matrix(y_true, y_pred, labels=None, title=None, normalize=False, x_tick_rotation=0,
+                          ax=None, figsize=None, title_fontsize="large", text_fontsize="medium"):
     """Generates confusion matrix plot for a given set of ground truth labels and classifier predictions.
 
     Args:
@@ -44,6 +44,9 @@ def plot_confusion_matrix(y_true, y_pred, labels=None, title=None, normalize=Fal
         normalize (bool, optional): If True, normalizes the confusion matrix before plotting.
             Defaults to False.
 
+        x_tick_rotation (int, optional): Rotates x-axis tick labels by the specified angle. This is
+            useful in cases where there are numerous categories and the labels overlap each other.
+
         ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to plot
             the learning curve. If None, the plot is drawn on a new set of axes.
 
@@ -96,7 +99,7 @@ def plot_confusion_matrix(y_true, y_pred, labels=None, title=None, normalize=Fal
     plt.colorbar(mappable=image)
     tick_marks = np.arange(len(classes))
     ax.set_xticks(tick_marks)
-    ax.set_xticklabels(classes, fontsize=text_fontsize)
+    ax.set_xticklabels(classes, fontsize=text_fontsize, rotation=x_tick_rotation)
     ax.set_yticks(tick_marks)
     ax.set_yticklabels(classes, fontsize=text_fontsize)
 
diff --git a/setup.py b/setup.py
index bdf07cd..b18a76a 100644
--- a/setup.py
+++ b/setup.py
@@ -6,8 +6,6 @@
 import os
 import sys
 
-import scikitplot
-
 here = os.path.abspath(os.path.dirname(__file__))
 
 
@@ -37,7 +35,7 @@ def run_tests(self):
 
 setup(
     name='scikit-plot',
-    version=scikitplot.__version__,
+    version='0.2.7',
     url='https://github.com/reiinakano/scikit-plot',
     license='MIT License',
     author='Reiichiro Nakano',