Class AdaBoostRegressorScikitsLearnNode

An AdaBoost regressor.

This node has been automatically generated by wrapping the ``sklearn.ensemble.weight_boosting.AdaBoostRegressor`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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 :ref:`User Guide <adaboost>`.

**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**

.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
       on-Line Learning and an Application to Boosting", 1995.

.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.

Overrides: object.__init__

Predict regression value for X.

This node has been automatically generated by wrapping the sklearn.ensemble.weight_boosting.AdaBoostRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

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

This node has been automatically generated by wrapping the sklearn.ensemble.weight_boosting.AdaBoostRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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.

Overrides: Node.stop_training