Pushing the docs to dev/ for branch: master, commit b026c4d7c98935c097fa191f4a7ca799160c91d7

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -83,7 +83,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\"Features selected by forward sequential selection: \"\n      f\"{feature_names[sfs.get_support().tolist()]}\")"
+        "from sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\"Features selected by forward sequential selection: \"\n      f\"{feature_names[sfs.get_support()].tolist()}\")"
       ]
     },
     {
@@ -101,7 +101,25 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import fetch_covtype\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(MinMaxScaler(),\n                     PolynomialCountSketch(degree=2, n_components=300),\n                     LogisticRegression(max_iter=1000))\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=5000,\n                                                    test_size=10000,\n                                                    random_state=42)\npipe.fit(X_train, y_train).score(X_test, y_test)\n\n# ##############################################################################\n# # For comparison, here is the score of a linear baseline for the same data:\n\nlinear_baseline = make_pipeline(MinMaxScaler(),\n                                LogisticRegression(max_iter=1000))\nlinear_baseline.fit(X_train, y_train).score(X_test, y_test)"
+        "from sklearn.datasets import fetch_covtype\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(MinMaxScaler(),\n                     PolynomialCountSketch(degree=2, n_components=300),\n                     LogisticRegression(max_iter=1000))\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=5000,\n                                                    test_size=10000,\n                                                    random_state=42)\npipe.fit(X_train, y_train).score(X_test, y_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For comparison, here is the score of a linear baseline for the same data:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "linear_baseline = make_pipeline(MinMaxScaler(),\n                                LogisticRegression(max_iter=1000))\nlinear_baseline.fit(X_train, y_train).score(X_test, y_test)"
       ]
     },
     {

dev/_downloads/2e6d841155147c9fbce4ea3837b924b9/plot_release_highlights_0_24_0.ipynb

@@ -146,7 +146,7 @@
 sfs = SequentialFeatureSelector(knn, n_features_to_select=2)
 sfs.fit(X, y)
 print("Features selected by forward sequential selection: "
-      f"{feature_names[sfs.get_support().tolist()]}")
+      f"{feature_names[sfs.get_support()].tolist()}")
 
 ##############################################################################
 # New PolynomialCountSketch kernel approximation function
@@ -172,8 +172,8 @@
                                                     random_state=42)
 pipe.fit(X_train, y_train).score(X_test, y_test)
 
-# ##############################################################################
-# # For comparison, here is the score of a linear baseline for the same data:
+##############################################################################
+# For comparison, here is the score of a linear baseline for the same data:
 
 linear_baseline = make_pipeline(MinMaxScaler(),
                                 LogisticRegression(max_iter=1000))