An AdaBoost classifier.

An AdaBoost 1 classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

This class implements the algorithm known as AdaBoost-SAMME 2.

Read more in the :ref:`User Guide <adaboost>`.

.. versionadded:: 0.14

Parameters ---------- base_estimator : object, default=None The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper ``classes_`` and ``n_classes_`` attributes. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)``.

n_estimators : int, default=50 The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, default=1. Learning rate shrinks the contribution of each classifier by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators``.

algorithm : 'SAMME', 'SAMME.R', default='SAMME.R' If 'SAMME.R' then use the SAMME.R real boosting algorithm. ``base_estimator`` must support calculation of class probabilities. If 'SAMME' then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.

random_state : int or RandomState, default=None Controls the random seed given at each `base_estimator` at each boosting iteration. Thus, it is only used when `base_estimator` exposes a `random_state`. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

Attributes ---------- base_estimator_ : estimator The base estimator from which the ensemble is grown.

estimators_ : list of classifiers The collection of fitted sub-estimators.

classes_ : ndarray of shape (n_classes,) The classes labels.

n_classes_ : int The number of classes.

estimator_weights_ : ndarray of floats Weights for each estimator in the boosted ensemble.

estimator_errors_ : ndarray of floats Classification error for each estimator in the boosted ensemble.

feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances if supported by the ``base_estimator`` (when based on decision trees).

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative.

See Also -------- AdaBoostRegressor An AdaBoost regressor that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction.

GradientBoostingClassifier GB builds an additive model in a forward stage-wise fashion. Regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.

sklearn.tree.DecisionTreeClassifier A non-parametric supervised learning method used for classification. Creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

References ---------- .. 1 Y. Freund, R. Schapire, 'A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting', 1995.

.. 2 J. Zhu, H. Zou, S. Rosset, T. Hastie, 'Multi-class AdaBoost', 2009.

Examples -------- >>> from sklearn.ensemble import AdaBoostClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=1000, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = AdaBoostClassifier(n_estimators=100, random_state=0) >>> clf.fit(X, y) AdaBoostClassifier(n_estimators=100, random_state=0) >>> clf.predict([0, 0, 0, 0]) array(1) >>> clf.score(X, y) 0.983...

Return the index'th estimator in the ensemble.

Return iterator over estimators in the ensemble.

Compute the decision function of ``X``.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns ------- score : ndarray of shape of (n_samples, k) The decision function of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute. Binary classification is a special cases with ``k == 1``, otherwise ``k==n_classes``. For binary classification, values closer to -1 or 1 mean more like the first or second class in ``classes_``, respectively.

Build a boosted classifier from the training set (X, y).

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

y : array-like of shape (n_samples,) The target values (class labels).

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, the sample weights are initialized to ``1 / n_samples``.

Returns ------- self : object Fitted estimator.

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns ------- y : ndarray of shape (n_samples,) The predicted classes.

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Compute decision function of ``X`` for each boosting iteration.

This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields ------ score : generator of ndarray of shape (n_samples, k) The decision function of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute. Binary classification is a special cases with ``k == 1``, otherwise ``k==n_classes``. For binary classification, values closer to -1 or 1 mean more like the first or second class in ``classes_``, respectively.

Return staged predictions for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields ------ y : generator of ndarray of shape (n_samples,) The predicted classes.

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities on a test set after each boost.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields ------- p : generator of ndarray of shape (n_samples,) The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

Return staged scores for X, y.

This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

y : array-like of shape (n_samples,) Labels for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Yields ------ z : float

Attribute base_estimator_: get value or raise Not_found if None.

Attribute base_estimator_: get value as an option.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: get value as an option.

Attribute classes_: get value or raise Not_found if None.

Attribute classes_: get value as an option.

Attribute n_classes_: get value or raise Not_found if None.

Attribute n_classes_: get value as an option.

Attribute estimator_weights_: get value or raise Not_found if None.

Attribute estimator_weights_: get value as an option.

Attribute estimator_errors_: get value or raise Not_found if None.

Attribute estimator_errors_: get value as an option.

Attribute feature_importances_: get value or raise Not_found if None.

Attribute feature_importances_: get value as an option.

Attribute Warning: get value or raise Not_found if None.

Attribute Warning: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.