8000 MNT Deprecate SAMME.R algorithm from AdaBoostClassifier (#26830) · scikit-learn/scikit-learn@c634b8a · GitHub
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StefanieSengerglemaitre
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glemaitre
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MNT Deprecate SAMME.R algorithm from AdaBoostClassifier (#26830)
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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‎benchmarks/bench_20newsgroups.py

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"extra_trees": ExtraTreesClassifier(max_features="sqrt", min_samples_split=10),
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"logistic_regression": LogisticRegression(),
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"naive_bayes": MultinomialNB(),
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"adaboost": AdaBoostClassifier(n_estimators=10),
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"adaboost": AdaBoostClassifier(n_estimators=10, algorithm="SAMME"),
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}
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‎doc/conf.py

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"auto_examples/decomposition/plot_beta_divergence": (
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"auto_examples/applications/plot_topics_extraction_with_nmf_lda"
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),
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"auto_examples/ensemble/plot_adaboost_hastie_10_2": (
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"auto_examples/ensemble/plot_adaboost_multiclass"
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),
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}
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html_context["redirects"] = redirects
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for old_link in redirects:

‎doc/modules/ensemble.rst

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@@ -1570,15 +1570,15 @@ ever-increasing influence. Each subsequent weak learner is thereby forced to
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concentrate on the examples that are missed by the previous ones in the sequence
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[HTF]_.
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.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_adaboost_hastie_10_2_001.png
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:target: ../auto_examples/ensemble/plot_adaboost_hastie_10_2.html
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.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_adaboost_multiclass_001.png
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:target: ../auto_examples/ensemble/plot_adaboost_multiclass.html
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:align: center
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:scale: 75
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AdaBoost can be used both for classification and regression problems:
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- For multi-class classification, :class:`AdaBoostClassifier` implements
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AdaBoost-SAMME and AdaBoost-SAMME.R [ZZRH2009]_.
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AdaBoost.SAMME [ZZRH2009]_.
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- For regression, :class:`AdaBoostRegressor` implements AdaBoost.R2 [D1997]_.
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>>> from sklearn.ensemble import AdaBoostClassifier
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>>> X, y = load_iris(return_X_y=True)
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>>> clf = AdaBoostClassifier(n_estimators=100)
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>>> clf = AdaBoostClassifier(n_estimators=100, algorithm="SAMME",)
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>>> scores = cross_val_score(clf, X, y, cv=5)
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>>> scores.mean()
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0.9...
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_hastie_10_2.py` compares the
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classification error of a decision stump, decision tree, and a boosted
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decision stump using AdaBoost-SAMME and AdaBoost-SAMME.R.
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* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` shows the performance
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of AdaBoost-SAMME and AdaBoost-SAMME.R on a multi-class problem.
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of AdaBoost on a multi-class problem.
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* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` shows the decision boundary
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and decision function values for a non-linearly separable two-class problem
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.. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of
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Statistical Learning Ed. 2", Springer, 2009.
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‎doc/whats_new/v1.4.rst

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:pr:`13649` by :user:`Samuel Ronsin <samronsin>`,
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initiated by :user:`Patrick O'Reilly <pat-oreilly>`.
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- |API| In :class:`AdaBoostClassifier`, the `algorithm` argument `SAMME.R` was
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deprecated and will be removed in 1.6. :pr:`26830` by :user:`Stefanie Senger
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<StefanieSenger>`.
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- |Efficiency| :class:`ensemble.GradientBoostingClassifier` is faster,
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for binary and in particular for multiclass problems thanks to the private loss
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function module.

‎examples/classification/plot_classifier_comparison.py

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max_depth=5, n_estimators=10, max_features=1, random_state=42
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),
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MLPClassifier(alpha=1, max_iter=1000, random_state=42),
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AdaBoostClassifier(random_state=42),
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AdaBoostClassifier(algorithm="SAMME", random_state=42),
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GaussianNB(),
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QuadraticDiscriminantAnalysis(),
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]

‎examples/ensemble/plot_adaboost_hastie_10_2.py

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‎examples/ensemble/plot_forest_iris.py

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DecisionTreeClassifier(max_depth=None),
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RandomForestClassifier(n_estimators=n_estimators),
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ExtraTreesClassifier(n_estimators=n_estimators),
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AdaBoostClassifier(DecisionTreeClassifier(max_depth=3), n_estimators=n_estimators),
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AdaBoostClassifier(
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DecisionTreeClassifier(max_depth=3),
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n_estimators=n_estimators,
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algorithm="SAMME",
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),
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]
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