class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None)[source]

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.

Read more in the User Guide.


alphas : numpy array of shape [n_alphas]

Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 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. This parameter is ignored when fit_intercept is set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if you wish to standardize, please use preprocessing.StandardScaler before calling fit on an estimator with normalize=False.

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 : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the efficient Leave-One-Out cross-validation
  • integer, to specify the number of folds.
  • An object to be used as a cross-validation generator.
  • An iterable yielding train/test splits.

Refer User Guide for the various cross-validation strategies that can be used here.

class_weight : dict or ‘balanced’, optional

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

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))


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).

intercept_ : float | array, shape = (n_targets,)

Independent term in decision function. Set to 0.0 if fit_intercept = False.

alpha_ : float

Estimated regularization parameter

See also

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


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.


decision_function(X) Predict confidence scores for samples.
fit(X, y[, sample_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=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None)[source]

Predict confidence scores for samples.

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


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



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)[source]

Fit the ridge classifier.


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.


self : object

Returns self.


Get parameters for this estimator.


deep: boolean, optional :

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


params : mapping of string to any

Parameter names mapped to their values.


Predict class labels for samples in X.


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



C : array, shape = [n_samples]

Predicted class label per sample.

score(X, y, sample_weight=None)[source]

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

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.


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

Test samples.

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

True labels for X.

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

Sample weights.


score : float

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


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :