 sklearn.linear_model.RidgeClassifierCV   classsklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, normalize='deprecated', 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.  Deprecated since version 1.0: normalize was deprecated in version 1.0 and will be removed in 1.2.   
scoringstr, 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 (only if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors if scoring is None otherwise it will contain standardized per point prediction values.  
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,)


Classes labels.  
n_features_in_int


Number of features seen during fit.  New in version 0.24.   
feature_names_in_ndarray of shape (n_features_in_,)


Names of features seen during fit. Defined only when X has feature names that are all strings.  New in version 1.0.       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.     propertyclasses_
 
Classes labels. 
   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:
 
 
X{array-like, sparse matrix} of shape (n_samples, n_features)


The data matrix for which we want to get the confidence scores.    Returns:
 
 
scoresndarray of shape (n_samples,) or (n_samples, n_classes)


Confidence scores per (n_samples, n_classes) 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


Fitted estimator.     
   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:
 
 
X{array-like, spare matrix} of shape (n_samples, n_features)


The data matrix for which we want to predict the targets.    Returns:
 
 
y_predndarray of shape (n_samples,) or (n_samples, n_outputs)


Vector or matrix containing the predictions. In binary and multiclass problems, this is a vector containing n_samples. In a multilabel problem, it returns a matrix of shape (n_samples, n_outputs).     
   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.     
 
 

    
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