5.2.2.1.2. sklearn.linear_model.RidgeClassifierCV

class sklearn.linear_model.RidgeClassifierCV(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None)

Methods

decision_function(X)
fit(X, y[, sample_weight, class_weight])	Fit the ridge classifier.
predict(X)
score(X, y)	Returns the coefficient of determination of the prediction
set_params(**params)	Set the parameters of the estimator.

__init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None)

fit(X, y, sample_weight=1.0, class_weight=None)

Fit the ridge classifier.

Parameters :

X : array-like, 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.

class_weight : dict, optional

Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.

sample_weight : float or numpy array of shape [n_samples]

Sample weight

Returns :

self : object

Returns self.

score(X, y)

Returns the coefficient of determination of the prediction

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float

set_params(**params)

Set the parameters of the 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 :