a 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.

    .. versionadded:: 0.24

store_cv_values : bool
    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).

    .. deprecated:: 1.5
        `store_cv_values` is deprecated in version 1.5 in favor of
        `store_cv_results` and will be removed in version 1.7.

Attributes
----------
cv_results_ : 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_results=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.

    .. versionchanged:: 1.5
        `cv_values_` changed to `cv_results_`.

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.

    .. versionadded:: 0.23

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

See Also
--------
Ridge : Ridge regression.
RidgeClassifier : Classifier based on ridge regression on {-1, 1} labels.
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...
Tr