Pushing the docs for revision for branch: master, commit 3d0c8f8b68584efdf65ba3f1bd21925e65aad3c3

Author: sklearn-ci

dev/_downloads/plot_f_test_vs_mi.py

@@ -0,0 +1,49 @@
+"""
+===========================================
+Comparison of F-test and mutual information
+===========================================
+
+This example illustrates the differences between univariate F-test statistics
+and mutual information.
+
+We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
+target depends on them as follows:
+
+y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third features is completely irrelevant.
+
+The code below plots the dependency of y against individual x_i and normalized
+values of univariate F-tests statistics and mutual information.
+
+As F-test captures only linear dependency, it rates x_1 as the most
+discriminative feature. On the other hand, mutual information can capture any
+kind of dependency between variables and it rates x_2 as the most
+discriminative feature, which probably agrees better with our intuitive
+perception for this example. Both methods correctly marks x_3 as irrelevant.
+"""
+print(__doc__)
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.feature_selection import f_regression, mutual_info_regression
+
+np.random.seed(0)
+X = np.random.rand(1000, 3)
+y = X[:, 0] + np.sin(6 * np.pi * X[:, 1]) + 0.1 * np.random.randn(1000)
+
+f_test, _ = f_regression(X, y)
+f_test /= np.max(f_test)
+
+mi = mutual_info_regression(X, y)
+mi /= np.max(mi)
+
+plt.figure(figsize=(15, 5))
+for i in range(3):
+    plt.subplot(1, 3, i + 1)
+    plt.scatter(X[:, i], y)
+    plt.xlabel("$x_{}$".format(i + 1), fontsize=14)
+    if i == 0:
+        plt.ylabel("$y$", fontsize=14)
+    plt.title("F-test={:.2f}, MI={:.2f}".format(f_test[i], mi[i]),
+              fontsize=16)
+plt.show()
+

dev/_downloads/plot_rfe_digits.py

@@ -33,4 +33,4 @@
 plt.matshow(ranking, cmap=plt.cm.Blues)
 plt.colorbar()
 plt.title("Ranking of pixels with RFE")
-plt.show()
+plt.show()