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)
n_estimators : integer, optional (default=50)
learning_rate : float, optional (default=1.)
loss : {‘linear’, ‘square’, ‘exponential’}, optional (default=’linear’)
random_state : int, RandomState instance or None, optional (default=None)
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Attributes: |
estimators_ : list of classifiers
estimator_weights_ : array of floats
estimator_errors_ : array of floats
feature_importances_ : array of shape = [n_features]
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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] |
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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()