# AdaBoostRegressor¶

class ibex.sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None)

Bases: sklearn.ensemble.weight_boosting.AdaBoostRegressor, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute feature_importances_.

Example:

>>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.ensemble import RandomForestClassifier as PdRandomForestClassifier

>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])

>>> iris[features]
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
...

>>> clf =  PdRandomForestClassifier(random_state=42).fit(iris[features], iris['class'])
>>>
>>> clf.feature_importances_
sepal length (cm)    0.129268
sepal width (cm)     0.015822
petal length (cm)    0.444740
petal width (cm)     0.410169
dtype: float64


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.

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.
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.

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

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

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.
self : object
Returns self.
predict(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

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 of shape = [n_samples]
The predicted regression values.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns 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.

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.
score : float
R^2 of self.predict(X) wrt. y.
staged_predict(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

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 : generator of array, shape = [n_samples]
The predicted regression values.