`sklearn.ensemble`.AdaBoostRegressor¶

class sklearn.ensemble.AdaBoostRegressor(estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None, base_estimator='deprecated')[source]¶

An AdaBoost regressor.

An AdaBoost [1] regressor is a meta-estimator 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].

See also

AdaBoostClassifier: An AdaBoost classifier.
GradientBoostingRegressor: Gradient Boosting Classification Tree.
sklearn.tree.DecisionTreeRegressor: A decision tree regressor.

References

[1]

Y. Freund, R. Schapire, “A Decision-Theoretic Generalization of on-Line 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 classifier/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 of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`staged_predict`(X)	Return staged predictions for X.
`staged_score`(X, y[, sample_weight])	Return staged scores for X, y.

property base_estimator_¶: Estimator used to grow the ensemble.

property feature_importances_¶

The impurity-based 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: impurity-based 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 classifier/regressor 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.
yarray-like of shape (n_samples,): The target values.
sample_weightarray-like 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(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:

paramsdict: 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 regressors 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:

yndarray of shape (n_samples,): The predicted regression values.

score(X, y, sample_weight=None)[source]¶

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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:

Xarray-like 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 with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: \(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). 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:

selfestimator instance: 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 regressors 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, 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{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.
yarray-like of shape (n_samples,): Labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Yields:

zfloat

Examples using `sklearn.ensemble.AdaBoostRegressor`¶

Decision Tree Regression with AdaBoost

sklearn.ensemble.AdaBoostRegressor¶

Examples using sklearn.ensemble.AdaBoostRegressor¶

`sklearn.ensemble`.AdaBoostRegressor¶

Examples using `sklearn.ensemble.AdaBoostRegressor`¶