Pushing the docs to dev/ for branch: main, commit c08afded996d08a7dde8441708ed9ca4cbb40559

Author: sklearn-ci

dev/.buildinfo

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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9b7d2f79dd6a18e64875292a9cfb6796
+config: 8c04582673397c4c20a90bb3f93b2d44
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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 # ---------------------------------
 # We train the :class:`~sklearn.ensemble.AdaBoostClassifier`. The estimator
 # utilizes boosting to improve the classification accuracy. Boosting is a method
-# designed to train weak learners (i.e. `base_estimator`) that learn from their
+# designed to train weak learners (i.e. `estimator`) that learn from their
 # predecessor's mistakes.
 #
 # Here, we define the weak learner as a

dev/_downloads/4e46f015ab8300f262e6e8775bcdcf8a/plot_adaboost_multiclass.py

@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Training the `AdaBoostClassifier`\nWe train the :class:`~sklearn.ensemble.AdaBoostClassifier`. The estimator\nutilizes boosting to improve the classification accuracy. Boosting is a method\ndesigned to train weak learners (i.e. `base_estimator`) that learn from their\npredecessor's mistakes.\n\nHere, we define the weak learner as a\n:class:`~sklearn.tree.DecisionTreeClassifier` and set the maximum number of\nleaves to 8. In a real setting, this parameter should be tuned. We set it to a\nrather low value to limit the runtime of the example.\n\nThe `SAMME` algorithm build into the\n:class:`~sklearn.ensemble.AdaBoostClassifier` then uses the correct or\nincorrect predictions made be the current weak learner to update the sample\nweights used for training the consecutive weak learners. Also, the weight of\nthe weak learner itself is calculated based on its accuracy in classifying the\ntraining examples. The weight of the weak learner determines its influence on\nthe final ensemble prediction.\n\n"
+        "## Training the `AdaBoostClassifier`\nWe train the :class:`~sklearn.ensemble.AdaBoostClassifier`. The estimator\nutilizes boosting to improve the classification accuracy. Boosting is a method\ndesigned to train weak learners (i.e. `estimator`) that learn from their\npredecessor's mistakes.\n\nHere, we define the weak learner as a\n:class:`~sklearn.tree.DecisionTreeClassifier` and set the maximum number of\nleaves to 8. In a real setting, this parameter should be tuned. We set it to a\nrather low value to limit the runtime of the example.\n\nThe `SAMME` algorithm build into the\n:class:`~sklearn.ensemble.AdaBoostClassifier` then uses the correct or\nincorrect predictions made be the current weak learner to update the sample\nweights used for training the consecutive weak learners. Also, the weight of\nthe weak learner itself is calculated based on its accuracy in classifying the\ntraining examples. The weight of the weak learner determines its influence on\nthe final ensemble prediction.\n\n"
       ]
     },
     {