Class VotingClassifierScikitsLearnNode

Soft Voting/Majority Rule classifier for unfitted estimators.

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

.. versionadded:: 0.17

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

**Parameters**

estimators : list of (string, estimator) tuples
    Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones
    of those original estimators that will be stored in the class attribute
    `self.estimators_`.

voting : str, {'hard', 'soft'} (default='hard')
    If 'hard', uses predicted class labels for majority rule voting.
    Else if 'soft', predicts the class label based on the argmax of
    the sums of the predicted probalities, which is recommended for
    an ensemble of well-calibrated classifiers.

weights : array-like, shape = [n_classifiers], optional (default=`None`)
    Sequence of weights (`float` or `int`) to weight the occurances of
    predicted class labels (`hard` voting) or class probabilities
    before averaging (`soft` voting). Uses uniform weights if `None`.

**Attributes**

``classes_`` : array-like, shape = [n_predictions]

**Examples**

>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.ensemble import RandomForestClassifier
>>> clf1 = LogisticRegression(random_state=1)
>>> clf2 = RandomForestClassifier(random_state=1)
>>> clf3 = GaussianNB()
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> eclf1 = VotingClassifier(estimators=[
...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')
>>> eclf1 = eclf1.fit(X, y)
>>> print(eclf1.predict(X))
[1 1 1 2 2 2]
>>> eclf2 = VotingClassifier(estimators=[
...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
...         voting='soft')
>>> eclf2 = eclf2.fit(X, y)
>>> print(eclf2.predict(X))
[1 1 1 2 2 2]
>>> eclf3 = VotingClassifier(estimators=[
...        ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
...        voting='soft', weights=[2,1,1])
>>> eclf3 = eclf3.fit(X, y)
>>> print(eclf3.predict(X))
[1 1 1 2 2 2]
>>>

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class labels for X.

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

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

maj : array-like, shape = [n_samples]

Predicted class labels.

Overrides: ClassifierNode.label

Fit the estimators.

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

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples]

Target values.

Returns

self : object

Overrides: Node.stop_training