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 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].

Read more in the User Guide.

New in version 0.14.

Parameters
base_estimatorobject, optional (default=None)

The base estimator from which the boosted ensemble is built. If None, then the base estimator is DecisionTreeRegressor(max_depth=3).

n_estimatorsinteger, optional (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, optional (default=1.)

Learning rate shrinks the contribution of each regressor by learning_rate. There is a trade-off between learning_rate and n_estimators.

loss{‘linear’, ‘square’, ‘exponential’}, optional (default=’linear’)

The loss function to use when updating the weights after each boosting iteration.

random_stateint, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes
base_estimator_estimator

The base estimator from which the ensemble is grown.

estimators_list of classifiers

The collection of fitted sub-estimators.

estimator_weights_array of floats

Weights for each estimator in the boosted ensemble.

estimator_errors_array of floats

Regression error for each estimator in the boosted ensemble.

feature_importances_ndarray of shape (n_features,)

Return the feature importances (the higher, the more important the feature).

See also

AdaBoostClassifier, GradientBoostingRegressor
sklearn.tree.DecisionTreeRegressor

References

R0c261b7dee9d-1

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

R0c261b7dee9d-2

  1. 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.feature_importances_
array([0.2788..., 0.7109..., 0.0065..., 0.0036...])
>>> regr.predict([[0, 0, 0, 0]])
array([4.7972...])
>>> regr.score(X, y)
0.9771...

Methods

fit(self, X, y[, sample_weight])

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

get_params(self[, deep])

Get parameters for this estimator.

predict(self, X)

Predict regression value for X.

score(self, X, y[, sample_weight])

Return the coefficient of determination R^2 of the prediction.

set_params(self, \*\*params)

Set the parameters of this estimator.

staged_predict(self, X)

Return staged predictions 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, loss='linear', random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

property feature_importances_
Return the feature importances (the higher, the more important the

feature).

Returns
feature_importances_ndarray of shape (n_features,)

The feature importances.

fit(self, X, y, sample_weight=None)[source]

Build a boosted 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 (real numbers).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, the sample weights are initialized to 1 / n_samples.

Returns
selfobject
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 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{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(self, 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
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, 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) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

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_predict(self, 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{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples.

Yields
ygenerator of array, shape = [n_samples]

The predicted regression values.

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{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