5.2.3.1.2. 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, gcv_mode=None)

Methods

decision_function(X)
fit(X, y[, sample_weight, class_weight])	Fit the ridge classifier.
get_params([deep])	Get parameters for the estimator
predict(X)	Predict target values according to the fitted model.
score(X, y)	Returns the coefficient of determination R^2 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, gcv_mode=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.

sample_weight : float or numpy array of shape [n_samples]

Sample weight

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.

Returns :

self : object

Returns self.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Predict target values according to the fitted model.

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

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

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 :