Class AdaBoostClassifierScikitsLearnNode

An AdaBoost classifier.

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

An AdaBoost [1] classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.

This class implements the algorithm known as AdaBoost-SAMME [2].

Read more in the :ref:`User Guide <adaboost>`.

**Parameters**

base_estimator : object, optional (default=DecisionTreeClassifier)
    The base estimator from which the boosted ensemble is built.
    Support for sample weighting is required, as well as proper `classes_`
    and `n_classes_` attributes.

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 classifier by
    ``learning_rate``. There is a trade-off between ``learning_rate`` and
    ``n_estimators``.

algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')
    If 'SAMME.R' then use the SAMME.R real boosting algorithm.
    ``base_estimator`` must support calculation of class probabilities.
    If 'SAMME' then use the SAMME discrete boosting algorithm.
    The SAMME.R algorithm typically converges faster than SAMME,
    achieving a lower test error with fewer boosting iterations.

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.

``classes_`` : array of shape = [n_classes]
    The classes labels.

``n_classes_`` : int
    The number of classes.

``estimator_weights_`` : array of floats
    Weights for each estimator in the boosted ensemble.

``estimator_errors_`` : array of floats
    Classification 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

AdaBoostRegressor, GradientBoostingClassifier, DecisionTreeClassifier

**References**

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

.. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict classes for X.

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

The predicted class of an input sample is computed as the weighted mean 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 classes.

Overrides: ClassifierNode.label

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

This node has been automatically generated by wrapping the sklearn.ensemble.weight_boosting.AdaBoostClassifier 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 (class labels).

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