Pushing the docs to dev/ for branch: main, commit 398a7d3f4f008ff9f807ce425567c94947aba02c

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -21,8 +21,7 @@
 
 # Create the RFE object and compute a cross-validated score.
 svc = SVC(kernel="linear")
-# The "accuracy" scoring is proportional to the number of correct
-# classifications
+# The "accuracy" scoring shows the proportion of correct classifications
 
 min_features_to_select = 1  # Minimum number of features to consider
 rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),
@@ -35,7 +34,7 @@
 # Plot number of features VS. cross-validation scores
 plt.figure()
 plt.xlabel("Number of features selected")
-plt.ylabel("Cross validation score (nb of correct classifications)")
+plt.ylabel("Cross validation score (accuracy)")
 plt.plot(range(min_features_to_select,
                len(rfecv.grid_scores_) + min_features_to_select),
          rfecv.grid_scores_)

dev/_downloads/592b2521e44501266ca5339d1fb123cb/plot_rfe_with_cross_validation.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.datasets import make_classification\n\n# Build a classification task using 3 informative features\nX, y = make_classification(n_samples=1000, n_features=25, n_informative=3,\n                           n_redundant=2, n_repeated=0, n_classes=8,\n                           n_clusters_per_class=1, random_state=0)\n\n# Create the RFE object and compute a cross-validated score.\nsvc = SVC(kernel=\"linear\")\n# The \"accuracy\" scoring is proportional to the number of correct\n# classifications\n\nmin_features_to_select = 1  # Minimum number of features to consider\nrfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),\n              scoring='accuracy',\n              min_features_to_select=min_features_to_select)\nrfecv.fit(X, y)\n\nprint(\"Optimal number of features : %d\" % rfecv.n_features_)\n\n# Plot number of features VS. cross-validation scores\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Cross validation score (nb of correct classifications)\")\nplt.plot(range(min_features_to_select,\n               len(rfecv.grid_scores_) + min_features_to_select),\n         rfecv.grid_scores_)\nplt.show()"
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.datasets import make_classification\n\n# Build a classification task using 3 informative features\nX, y = make_classification(n_samples=1000, n_features=25, n_informative=3,\n                           n_redundant=2, n_repeated=0, n_classes=8,\n                           n_clusters_per_class=1, random_state=0)\n\n# Create the RFE object and compute a cross-validated score.\nsvc = SVC(kernel=\"linear\")\n# The \"accuracy\" scoring shows the proportion of correct classifications\n\nmin_features_to_select = 1  # Minimum number of features to consider\nrfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),\n              scoring='accuracy',\n              min_features_to_select=min_features_to_select)\nrfecv.fit(X, y)\n\nprint(\"Optimal number of features : %d\" % rfecv.n_features_)\n\n# Plot number of features VS. cross-validation scores\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Cross validation score (accuracy)\")\nplt.plot(range(min_features_to_select,\n               len(rfecv.grid_scores_) + min_features_to_select),\n         rfecv.grid_scores_)\nplt.show()"
       ]
     }
   ],