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3.2.3.1.2. sklearn.linear_model.RidgeClassifierCV

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

Ridge classifier with built-in cross-validation.

By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Currently, only the n_features > n_samples case is handled efficiently.

Parameters:

alphas: numpy array of shape [n_alphas] :

Array of alpha values to try. Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to (2*C)^-1 in other linear models such as LogisticRegression or LinearSVC.

fit_intercept : boolean

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).

normalize : boolean, optional, default False

If True, the regressors X will be normalized before regression.

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

cv : cross-validation generator, optional

If None, Generalized Cross-Validation (efficient Leave-One-Out) will be used.

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.

See also

Ridge
Ridge regression
RidgeClassifier
Ridge classifier
RidgeCV
Ridge regression with built-in cross validation

Notes

For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.

Attributes

cv_values_ array, shape = [n_samples, n_alphas] or shape = [n_samples, n_responses, n_alphas], optional Cross-validation values for each alpha (if store_cv_values=True and
cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).    
coef_ array, shape = [n_features] or [n_targets, n_features] Weight vector(s).
alpha_ float Estimated regularization parameter

Methods

decision_function(X) Predict confidence scores for samples.
fit(X, y[, sample_weight, class_weight]) Fit the ridge classifier.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict class labels for samples in X.
score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
__init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, scoring=None, score_func=None, loss_func=None, cv=None, class_weight=None)
decision_function(X)

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:

X : {array-like, sparse matrix}, shape = (n_samples, n_features)

Samples.

Returns:

array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) :

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

fit(X, y, sample_weight=None, 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. This is parameter is deprecated.

Returns:

self : object

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

Predict class labels for samples in X.

Parameters:

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

Samples.

Returns:

C : array, shape = [n_samples]

Predicted class label per sample.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples,)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)

Set the parameters of this 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 :
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