Pushing the docs to dev/ for branch: master, commit 549514e4fdc59d925746b5dc9bd9383781923726

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import svm\nfrom sklearn.datasets import make_classification\nfrom sklearn.feature_selection import SelectKBest, f_regression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\nprint(__doc__)\n\n# import some data to play with\nX, y = make_classification(\n    n_features=20, n_informative=3, n_redundant=0, n_classes=4,\n    n_clusters_per_class=2)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\n# ANOVA SVM-C\n# 1) anova filter, take 3 best ranked features\nanova_filter = SelectKBest(f_regression, k=3)\n# 2) svm\nclf = svm.LinearSVC()\n\nanova_svm = make_pipeline(anova_filter, clf)\nanova_svm.fit(X_train, y_train)\ny_pred = anova_svm.predict(X_test)\nprint(classification_report(y_test, y_pred))\n\ncoef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)\nprint(coef)"
+        "from sklearn import svm\nfrom sklearn.datasets import make_classification\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\nprint(__doc__)\n\n# import some data to play with\nX, y = make_classification(\n    n_features=20, n_informative=3, n_redundant=0, n_classes=4,\n    n_clusters_per_class=2)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\n# ANOVA SVM-C\n# 1) anova filter, take 3 best ranked features\nanova_filter = SelectKBest(f_classif, k=3)\n# 2) svm\nclf = svm.LinearSVC()\n\nanova_svm = make_pipeline(anova_filter, clf)\nanova_svm.fit(X_train, y_train)\ny_pred = anova_svm.predict(X_test)\nprint(classification_report(y_test, y_pred))\n\ncoef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)\nprint(coef)"
       ]
     }
   ],

dev/_downloads/51e6f272e94e3b63cfd48c4b41fbaa10/plot_feature_selection_pipeline.ipynb

@@ -11,7 +11,7 @@
 """
 from sklearn import svm
 from sklearn.datasets import make_classification
-from sklearn.feature_selection import SelectKBest, f_regression
+from sklearn.feature_selection import SelectKBest, f_classif
 from sklearn.pipeline import make_pipeline
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import classification_report
@@ -27,7 +27,7 @@
 
 # ANOVA SVM-C
 # 1) anova filter, take 3 best ranked features
-anova_filter = SelectKBest(f_regression, k=3)
+anova_filter = SelectKBest(f_classif, k=3)
 # 2) svm
 clf = svm.LinearSVC()