Pushing the docs to dev/ for branch: main, commit b4eef2579b62b7e77a8c2c556e934c17092e04a4

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.calibration import CalibratedClassifierCV\n\nclf = RandomForestClassifier(n_estimators=25)\nclf.fit(X_train, y_train)\ncal_clf = CalibratedClassifierCV(clf, method=\"sigmoid\", cv=\"prefit\")\ncal_clf.fit(X_valid, y_valid)"
+        "from sklearn.calibration import CalibratedClassifierCV\nfrom sklearn.frozen import FrozenEstimator\n\nclf = RandomForestClassifier(n_estimators=25)\nclf.fit(X_train, y_train)\ncal_clf = CalibratedClassifierCV(FrozenEstimator(clf), method=\"sigmoid\")\ncal_clf.fit(X_valid, y_valid)"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -64,10 +64,11 @@ class of an instance (red: class 1, green: class 2, blue: class 3).
 # using the valid data subset (400 samples) in a 2-stage process.
 
 from sklearn.calibration import CalibratedClassifierCV
+from sklearn.frozen import FrozenEstimator
 
 clf = RandomForestClassifier(n_estimators=25)
 clf.fit(X_train, y_train)
-cal_clf = CalibratedClassifierCV(clf, method="sigmoid", cv="prefit")
+cal_clf = CalibratedClassifierCV(FrozenEstimator(clf), method="sigmoid")
 cal_clf.fit(X_valid, y_valid)
 
 # %%