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diff --git a/‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
Lines changed: 15 additions & 15 deletions b/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
Lines changed: 15 additions & 15 deletions
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "numeric_features = ['age', 'fare']\nnumeric_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='median')),\n    ('scaler', StandardScaler())])\n\ncategorical_features = ['embarked', 'sex', 'pclass']\ncategorical_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('num', numeric_transformer, numeric_features),\n        ('cat', categorical_transformer, categorical_features)])\n\n# Append classifier to preprocessing pipeline.\n# Now we have a full prediction pipeline.\nclf = Pipeline(steps=[('preprocessor', preprocessor),\n                      ('classifier', LogisticRegression())])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n                                                    random_state=0)\n\nclf.fit(X_train, y_train)\nprint(\"model score: %.3f\" % clf.score(X_test, y_test))"
+        "numeric_features = ['age', 'fare']\nnumeric_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='median')),\n    ('scaler', StandardScaler())])\n\ncategorical_features = ['embarked', 'sex', 'pclass']\ncategorical_transformer = OneHotEncoder(handle_unknown='ignore')\n\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('num', numeric_transformer, numeric_features),\n        ('cat', categorical_transformer, categorical_features)])\n\n# Append classifier to preprocessing pipeline.\n# Now we have a full prediction pipeline.\nclf = Pipeline(steps=[('preprocessor', preprocessor),\n                      ('classifier', LogisticRegression())])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n                                                    random_state=0)\n\nclf.fit(X_train, y_train)\nprint(\"model score: %.3f\" % clf.score(X_test, y_test))"
       ]
     },
     {
 
@@ -63,16 +63,13 @@
 X_train, X_test, y_train, y_test = train_test_split(
     X, y, stratify=y, random_state=42)
 
-categorical_pipe = Pipeline([
-    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
-    ('onehot', OneHotEncoder(handle_unknown='ignore'))
-])
+categorical_encoder = OneHotEncoder(handle_unknown='ignore')
 numerical_pipe = Pipeline([
     ('imputer', SimpleImputer(strategy='mean'))
 ])
 
 preprocessing = ColumnTransformer(
-    [('cat', categorical_pipe, categorical_columns),
+    [('cat', categorical_encoder, categorical_columns),
      ('num', numerical_pipe, numerical_columns)])
 
 rf = Pipeline([
@@ -122,8 +119,7 @@
 #   predictions that generalize to the test set (when the model has enough
 #   capacity).
 ohe = (rf.named_steps['preprocess']
-         .named_transformers_['cat']
-         .named_steps['onehot'])
+         .named_transformers_['cat'])
 feature_names = ohe.get_feature_names(input_features=categorical_columns)
 feature_names = np.r_[feature_names, numerical_columns]
 
 
@@ -72,9 +72,7 @@
     ('scaler', StandardScaler())])
 
 categorical_features = ['embarked', 'sex', 'pclass']
-categorical_transformer = Pipeline(steps=[
-    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
-    ('onehot', OneHotEncoder(handle_unknown='ignore'))])
+categorical_transformer = OneHotEncoder(handle_unknown='ignore')
 
 preprocessor = ColumnTransformer(
     transformers=[
 
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)\nrng = np.random.RandomState(seed=42)\nX['random_cat'] = rng.randint(3, size=X.shape[0])\nX['random_num'] = rng.randn(X.shape[0])\n\ncategorical_columns = ['pclass', 'sex', 'embarked', 'random_cat']\nnumerical_columns = ['age', 'sibsp', 'parch', 'fare', 'random_num']\n\nX = X[categorical_columns + numerical_columns]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, random_state=42)\n\ncategorical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n])\nnumerical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='mean'))\n])\n\npreprocessing = ColumnTransformer(\n    [('cat', categorical_pipe, categorical_columns),\n     ('num', numerical_pipe, numerical_columns)])\n\nrf = Pipeline([\n    ('preprocess', preprocessing),\n    ('classifier', RandomForestClassifier(random_state=42))\n])\nrf.fit(X_train, y_train)"
+        "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)\nrng = np.random.RandomState(seed=42)\nX['random_cat'] = rng.randint(3, size=X.shape[0])\nX['random_num'] = rng.randn(X.shape[0])\n\ncategorical_columns = ['pclass', 'sex', 'embarked', 'random_cat']\nnumerical_columns = ['age', 'sibsp', 'parch', 'fare', 'random_num']\n\nX = X[categorical_columns + numerical_columns]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, random_state=42)\n\ncategorical_encoder = OneHotEncoder(handle_unknown='ignore')\nnumerical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='mean'))\n])\n\npreprocessing = ColumnTransformer(\n    [('cat', categorical_encoder, categorical_columns),\n     ('num', numerical_pipe, numerical_columns)])\n\nrf = Pipeline([\n    ('preprocess', preprocessing),\n    ('classifier', RandomForestClassifier(random_state=42))\n])\nrf.fit(X_train, y_train)"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "ohe = (rf.named_steps['preprocess']\n         .named_transformers_['cat']\n         .named_steps['onehot'])\nfeature_names = ohe.get_feature_names(input_features=categorical_columns)\nfeature_names = np.r_[feature_names, numerical_columns]\n\ntree_feature_importances = (\n    rf.named_steps['classifier'].feature_importances_)\nsorted_idx = tree_feature_importances.argsort()\n\ny_ticks = np.arange(0, len(feature_names))\nfig, ax = plt.subplots()\nax.barh(y_ticks, tree_feature_importances[sorted_idx])\nax.set_yticklabels(feature_names[sorted_idx])\nax.set_yticks(y_ticks)\nax.set_title(\"Random Forest Feature Importances (MDI)\")\nfig.tight_layout()\nplt.show()"
+        "ohe = (rf.named_steps['preprocess']\n         .named_transformers_['cat'])\nfeature_names = ohe.get_feature_names(input_features=categorical_columns)\nfeature_names = np.r_[feature_names, numerical_columns]\n\ntree_feature_importances = (\n    rf.named_steps['classifier'].feature_importances_)\nsorted_idx = tree_feature_importances.argsort()\n\ny_ticks = np.arange(0, len(feature_names))\nfig, ax = plt.subplots()\nax.barh(y_ticks, tree_feature_importances[sorted_idx])\nax.set_yticklabels(feature_names[sorted_idx])\nax.set_yticks(y_ticks)\nax.set_title(\"Random Forest Feature Importances (MDI)\")\nfig.tight_layout()\nplt.show()"
       ]
     },
     {
Original file line number	Diff line number	Diff line change
`@@ -44,7 +44,7 @@`
`44`	`44`	`},`
`45`	`45`	`"outputs": [],`
`46`	`46`	`"source": [`
`47`		- "numeric_features = ['age', 'fare']\nnumeric_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())])\n\ncategorical_features = ['embarked', 'sex', 'pclass']\ncategorical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n\npreprocessor = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, numeric_features),\n ('cat', categorical_transformer, categorical_features)])\n\n# Append classifier to preprocessing pipeline.\n# Now we have a full prediction pipeline.\nclf = Pipeline(steps=[('preprocessor', preprocessor),\n ('classifier', LogisticRegression())])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n random_state=0)\n\nclf.fit(X_train, y_train)\nprint(\"model score: %.3f\" % clf.score(X_test, y_test))"
	`47`	+ "numeric_features = ['age', 'fare']\nnumeric_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())])\n\ncategorical_features = ['embarked', 'sex', 'pclass']\ncategorical_transformer = OneHotEncoder(handle_unknown='ignore')\n\npreprocessor = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, numeric_features),\n ('cat', categorical_transformer, categorical_features)])\n\n# Append classifier to preprocessing pipeline.\n# Now we have a full prediction pipeline.\nclf = Pipeline(steps=[('preprocessor', preprocessor),\n ('classifier', LogisticRegression())])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n random_state=0)\n\nclf.fit(X_train, y_train)\nprint(\"model score: %.3f\" % clf.score(X_test, y_test))"
`48`	`48`	`]`
`49`	`49`	`},`
`50`	`50`	`{`