8.17.25. sklearn.linear_model.RidgeCV

class sklearn.linear_model.RidgeCV(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, gcv_mode=None, store_cv_values=False)

Ridge regression with built-in cross-validation.

By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation.

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.

score_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (big is good) if None is passed, the score of the estimator is maximized

loss_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (small is good) if None is passed, the score of the estimator is maximized

cv : cross-validation generator, optional

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

gcv_mode : {None, ‘auto’, ‘svd’, eigen’}, optional

Flag indicating which strategy to use when performing Generalized Cross-Validation. Options are:

'auto' : use svd if n_samples > n_features, otherwise use eigen
'svd' : force computation via singular value decomposition of X
'eigen' : force computation via eigendecomposition of X^T X

The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending upon the shape of the training data.

store_cv_values : boolean, 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 Generalized Cross-Validation).

See also

Ridge

Ridge regression

RidgeClassifier

Ridge classifier

RidgeClassifierCV

Ridge classifier with built-in cross validation

Attributes

cv_values_	array, shape = [n_samples, n_alphas] or 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).
coef_	array, shape = [n_features] or [n_targets, n_features]	Weight vector(s).
alpha_	float	Estimated regularization parameter.

Methods

decision_function(X)	Decision function of the linear model
fit(X, y[, sample_weight])	Fit Ridge regression model
get_params([deep])	Get parameters for the estimator
predict(X)	Predict using the linear model
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of the estimator.

__init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, gcv_mode=None, store_cv_values=False)

decision_function(X)

Decision function of the linear model

Parameters:

X : numpy array of shape [n_samples, n_features]

Returns:

C : array, shape = [n_samples]

Returns predicted values.

fit(X, y, sample_weight=1.0)

Fit Ridge regression model

Parameters:

X : array-like, shape = [n_samples, n_features]

Training data

y : array-like, shape = [n_samples] or [n_samples, n_targets]

Target values

sample_weight : float or array-like of shape [n_samples]

Sample weight

Returns:

self : Returns self.

get_params(deep=True)

Get parameters for the estimator

Parameters:

deep: boolean, optional :

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

predict(X)

Predict using the linear model

Parameters:

X : numpy array of shape [n_samples, n_features]

Returns:

C : array, shape = [n_samples]

Returns predicted values.

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns:

z : float

set_params(**params)

Set the parameters of the 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 :