sklearn.ensemble
.AdaBoostRegressor¶

class
sklearn.ensemble.
AdaBoostRegressor
(base_estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None)[source]¶ An AdaBoost regressor.
An AdaBoost [1] regressor is a metaestimator 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. As such, subsequent regressors focus more on difficult cases.
This class implements the algorithm known as AdaBoost.R2 [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. If
None
, then the base estimator isDecisionTreeRegressor(max_depth=3)
. 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 regressor by
learning_rate
. There is a tradeoff betweenlearning_rate
andn_estimators
. loss{‘linear’, ‘square’, ‘exponential’}, default=’linear’
The loss function to use when updating the weights after each boosting iteration.
 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
. In addition, it controls the bootstrap of the weights used to train thebase_estimator
at each boosting iteration. 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.
 estimator_weights_ndarray of floats
Weights for each estimator in the boosted ensemble.
 estimator_errors_ndarray of floats
Regression error for each estimator in the boosted ensemble.
feature_importances_
ndarray of shape (n_features,)The impuritybased feature importances.
References
 1
Y. Freund, R. Schapire, “A DecisionTheoretic Generalization of onLine Learning and an Application to Boosting”, 1995.
 2
Drucker, “Improving Regressors using Boosting Techniques”, 1997.
Examples
>>> from sklearn.ensemble import AdaBoostRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=4, n_informative=2, ... random_state=0, shuffle=False) >>> regr = AdaBoostRegressor(random_state=0, n_estimators=100) >>> regr.fit(X, y) AdaBoostRegressor(n_estimators=100, random_state=0) >>> regr.predict([[0, 0, 0, 0]]) array([4.7972...]) >>> regr.score(X, y) 0.9771...
Methods
fit
(X, y[, sample_weight])Build a boosted regressor from the training set (X, y).
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict regression value for X.
score
(X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
set_params
(**params)Set the parameters of this estimator.
Return staged predictions for X.
staged_score
(X, y[, sample_weight])Return staged scores for X, y.

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

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
(X, y, sample_weight=None)[source]¶ Build a boosted regressor 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 (real numbers).
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights. If None, the sample weights are initialized to 1 / n_samples.
 Returns
 selfobject

get_params
(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
(X)[source]¶ Predict regression value for X.
The predicted regression value of an input sample is computed as the weighted median 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 regression values.

score
(X, y, sample_weight=None)[source]¶ Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**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_predict
(X)[source]¶ Return staged predictions for X.
The predicted regression value of an input sample is computed as the weighted median 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{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples.
 Yields
 ygenerator of ndarray of shape (n_samples,)
The predicted regression values.

staged_score
(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