class sklearn.linear_model.RidgeClassifierCV(alphas=0.1, 1.0, 10.0, *, fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False) [source]
 
Ridge classifier with built-in cross-validation. See glossary entry for cross-validation estimator. By default, it performs Leave-One-Out Cross-Validation. Currently, only the n_features > n_samples case is handled efficiently. Read more in the User Guide.  Parameters 
 
alphasndarray of shape (n_alphas,), default=(0.1, 1.0, 10.0) 

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 1 / (2C) in other linear models such as LogisticRegression or LinearSVC.  
fit_interceptbool, default=True 

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).  
normalizebool, default=False 

This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.  
scoringstring, callable, default=None 

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).  
cvint, cross-validation generator or an iterable, default=None 

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. 
CV splitter, An iterable yielding (train, test) splits as arrays of indices.  Refer User Guide for the various cross-validation strategies that can be used here.  
class_weightdict or ‘balanced’, default=None 

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))  
store_cv_valuesbool, default=False 

Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation).    Attributes 
 
cv_values_ndarray of shape (n_samples, n_targets, 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). This attribute exists only when store_cv_values is True.  
coef_ndarray of shape (1, n_features) or (n_targets, n_features) 

Coefficient of the features in the decision function. coef_ is of shape (1, n_features) when the given problem is binary.  
intercept_float or ndarray of shape (n_targets,) 

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

Estimated regularization parameter.  
best_score_float 

Score of base estimator with best alpha.  New in version 0.23.   
classes_ndarray of shape (n_classes,) 

The classes labels.      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. Examples >>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import RidgeClassifierCV
>>> X, y = load_breast_cancer(return_X_y=True)
>>> clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
>>> clf.score(X, y)
0.9630...
 Methods  
decision_function(X) Predict confidence scores for samples.  
fit(X, y[, sample_weight]) Fit Ridge classifier with cv.  
get_params([deep]) Get parameters for this estimator.  
predict(X) Predict class labels for samples in X.  
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.  
set_params(**params) Set the parameters of this estimator.    
decision_function(X) [source]
 
Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.  Parameters 
 
Xarray-like or 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) [source]
 
Fit Ridge classifier with cv.  Parameters 
 
Xndarray of shape (n_samples, n_features) 

Training vectors, where n_samples is the number of samples and n_features is the number of features. When using GCV, will be cast to float64 if necessary.  
yndarray of shape (n_samples,) 

Target values. Will be cast to X’s dtype if necessary.  
sample_weightfloat or ndarray of shape (n_samples,), default=None 

Individual weights for each sample. If given a float, every sample will have the same weight.    Returns 
 
selfobject 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

Parameter names mapped to their values.     
  
predict(X) [source]
 
Predict class labels for samples in X.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 
Carray, shape [n_samples] 

Predicted class label per sample.     
  
score(X, y, sample_weight=None) [source]
 
Return 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.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True labels for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

Mean accuracy of self.predict(X) wrt. y.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.