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.

Parameters:

base_estimator : object, optional (default=DecisionTreeRegressor) The base estimator from which the boosted ensemble is built. Support for sample weighting is required. n_estimators : integer, 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_rate : float, 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_state : int, 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:

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_ : array of shape = [n_features] The feature importances if supported by the base_estimator.

See also

AdaBoostClassifier, GradientBoostingRegressor, DecisionTreeRegressor

References

[R123]

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

[R124]

Drucker, “Improving Regressors using Boosting Techniques”, 1997.

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])	Returns the coefficient of determination R^2 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.

__init__(base_estimator=None, n_estimators=50, learning_rate=1.0, loss='linear',random_state=None)[source]

feature_importances_

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

feature).

Returns:	feature_importances_ : array, shape = [n_features]

fit(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. DOK and LIL are converted to CSR. y : array-like of shape = [n_samples] The target values (real numbers). sample_weight : array-like of shape = [n_samples], optional Sample weights. If None, the sample weights are initialized to 1 / n_samples.
Returns:	self : object Returns self.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:	deep: boolean, optional : 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(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. DOK and LIL are converted to CSR.
Returns:	y : array of shape = [n_samples] The predicted regression values.

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

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

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float R^2 of self.predict(X) wrt. y.

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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

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 : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. DOK and LIL are converted to CSR.
Returns:	y : generator of array, 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. DOK and LIL are converted to CSR. y : array-like, shape = [n_samples] Labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	z : float

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tree regressor. As the number of boosts is increased the regressor can fit more detail.

[1]	Drucker, “Improving Regressors using Boosting Techniques”, 1997.

print(__doc__)

# Author: Noel Dawe <noel.dawe@gmail.com>
#
# License: BSD 3 clause

# importing necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor

# Create the dataset
rng = np.random.RandomState(1)
X = np.linspace(0, 6, 100)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])

# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=4)

regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
                          n_estimators=300, random_state=rng)

regr_1.fit(X, y)
regr_2.fit(X, y)

# Predict
y_1 = regr_1.predict(X)
y_2 = regr_2.predict(X)

# Plot the results
plt.figure()
plt.scatter(X, y, c="k", label="training samples")
plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Boosted Decision Tree Regression")
plt.legend()
plt.show()