sklearn.linear_model.RidgeCV  
class sklearn.linear_model.RidgeCV(alphas=0.1, 1.0, 10.0, *, fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False, alpha_per_target=False) [source]
 
Ridge regression with built-in cross-validation. See glossary entry for cross-validation estimator. By default, it performs efficient Leave-One-Out Cross-Validation. 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. If using Leave-One-Out cross-validation, alphas must be positive.  
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). If None, the negative mean squared error if cv is ‘auto’ or None (i.e. when using leave-one-out cross-validation), and r2 score otherwise.  
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.  For integer/None inputs, if y is binary or multiclass, StratifiedKFold is used, else, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here.  
gcv_mode{‘auto’, ‘svd’, eigen’}, default=’auto’ 

Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are: 'auto' : use 'svd' if n_samples > n_features, otherwise use 'eigen'
'svd' : force use of singular value decomposition of X when X is
    dense, eigenvalue decomposition of X^T.X when X is sparse.
'eigen' : force computation via eigendecomposition of X.X^T
 The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending on the shape of the training data.  
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).  
alpha_per_targetbool, default=False 

Flag indicating whether to optimize the alpha value (picked from the alphas parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to True, after fitting, the alpha_ attribute will contain a value for each target. When set to False, a single alpha is used for all targets.  New in version 0.24.     Attributes 
 
cv_values_ndarray of shape (n_samples, n_alphas) or shape (n_samples, n_targets, n_alphas), optional 

Cross-validation values for each alpha (only available 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_ndarray of shape (n_features) or (n_targets, n_features) 

Weight vector(s).  
intercept_float or ndarray of shape (n_targets,) 

Independent term in decision function. Set to 0.0 if fit_intercept = False.  
alpha_float or ndarray of shape (n_targets,) 

Estimated regularization parameter, or, if alpha_per_target=True, the estimated regularization parameter for each target.  
best_score_float or ndarray of shape (n_targets,) 

Score of base estimator with best alpha, or, if alpha_per_target=True, a score for each target.  New in version 0.23.       See also  
Ridge


Ridge regression.  
RidgeClassifier


Ridge classifier.  
RidgeClassifierCV


Ridge classifier with built-in cross validation.    Examples >>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import RidgeCV
>>> X, y = load_diabetes(return_X_y=True)
>>> clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
>>> clf.score(X, y)
0.5166...
 Methods  
fit(X, y[, sample_weight]) Fit Ridge regression model with cv.  
get_params([deep]) Get parameters for this estimator.  
predict(X) Predict using the linear model.  
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y, sample_weight=None) [source]
 
Fit Ridge regression model with cv.  Parameters 
 
Xndarray of shape (n_samples, n_features) 

Training data. If using GCV, will be cast to float64 if necessary.  
yndarray of shape (n_samples,) or (n_samples, n_targets) 

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 
   Notes When sample_weight is provided, the selected hyperparameter may depend on whether we use leave-one-out cross-validation (cv=None or cv=’auto’) or another form of cross-validation, because only leave-one-out cross-validation takes the sample weights into account when computing the validation score. 
  
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 using the linear model.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 
Carray, shape (n_samples,) 

Returns predicted values.     
  
score(X, y, sample_weight=None) [source]
 
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

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

Sample weights.    Returns 
 
scorefloat 

\(R^2\) of self.predict(X) wrt. y.     Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). 
  
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.     
 
 Examples using sklearn.linear_model.RidgeCV
 
  Combine predictors using stacking  

  Common pitfalls in interpretation of coefficients of linear models  

  Face completion with a multi-output estimators  

  Effect of transforming the targets in regression model