sklearn.ensemble
.AdaBoostClassifier¶

class
sklearn.ensemble.
AdaBoostClassifier
(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)[source]¶ An AdaBoost classifier.
An AdaBoost [1] classifier is a metaestimator 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 AdaBoostSAMME [2].
Read more in the User Guide.
New in version 0.14.
 Parameters
 base_estimatorobject, default=None
The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper
classes_
andn_classes_
attributes. IfNone
, then the base estimator isDecisionTreeClassifier(max_depth=1)
. n_estimatorsint, default=50
The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.
 learning_ratefloat, default=1.
Learning rate shrinks the contribution of each classifier by
learning_rate
. There is a tradeoff betweenlearning_rate
andn_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_stateint or RandomState, default=None
Controls the random seed given at each
base_estimator
at each boosting iteration. Thus, it is only used whenbase_estimator
exposes arandom_state
. Pass an int for reproducible output across multiple function calls. See Glossary.
 Attributes
 base_estimator_estimator
The base estimator from which the ensemble is grown.
 estimators_list of classifiers
The collection of fitted subestimators.
 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 impuritybased feature importances.
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 stagewise 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 nonparametric 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
 R33e4ec8c4ad51
Y. Freund, R. Schapire, “A DecisionTheoretic Generalization of onLine Learning and an Application to Boosting”, 1995.
 R33e4ec8c4ad52
Zhu, H. Zou, S. Rosset, T. Hastie, “Multiclass 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...
Methods
decision_function
(self, X)Compute the decision function of
X
.fit
(self, X, y[, sample_weight])Build a boosted classifier from the training set (X, y).
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Predict classes for X.
predict_log_proba
(self, X)Predict class logprobabilities for X.
predict_proba
(self, X)Predict class probabilities for X.
score
(self, X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(self, \*\*params)Set the parameters of this estimator.
staged_decision_function
(self, X)Compute decision function of
X
for each boosting iteration.staged_predict
(self, X)Return staged predictions for X.
staged_predict_proba
(self, X)Predict class probabilities for X.
staged_score
(self, X, y[, sample_weight])Return staged scores for X, y.

__init__
(self, base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

decision_function
(self, X)[source]¶ Compute the decision function of
X
. Parameters
 X{arraylike, 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
 scorendarray of shape of (n_samples, k)
The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with
k == 1
, otherwisek==n_classes
. For binary classification, values closer to 1 or 1 mean more like the first or second class inclasses_
, respectively.

property
feature_importances_
¶ The impuritybased feature importances.
The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
The feature importances.

fit
(self, X, y, sample_weight=None)[source]¶ Build a boosted classifier from the training set (X, y).
 Parameters
 X{arraylike, 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.
 yarraylike of shape (n_samples,)
The target values (class labels).
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights. If None, the sample weights are initialized to
1 / n_samples
.
 Returns
 selfobject
Fitted estimator.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ 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{arraylike, 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
 yndarray of shape (n_samples,)
The predicted classes.

predict_log_proba
(self, X)[source]¶ Predict class logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the weighted mean predicted class logprobabilities of the classifiers in the ensemble.
 Parameters
 X{arraylike, 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
 pndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

predict_proba
(self, X)[source]¶ 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{arraylike, 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
 pndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

score
(self, X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **params)[source]¶ 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
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.

staged_decision_function
(self, X)[source]¶ 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{arraylike, 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
 scoregenerator 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 classes_ attribute. Binary classification is a special cases with
k == 1
, otherwisek==n_classes
. For binary classification, values closer to 1 or 1 mean more like the first or second class inclasses_
, respectively.

staged_predict
(self, X)[source]¶ 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
 Xarraylike 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
 ygenerator of ndarray of shape (n_samples,)
The predicted classes.

staged_predict_proba
(self, X)[source]¶ 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{arraylike, 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
 pgenerator of ndarray of shape (n_samples,)
The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

staged_score
(self, X, y, sample_weight=None)[source]¶ 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{arraylike, 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.
 yarraylike of shape (n_samples,)
Labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Yields
 zfloat