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# `sklearn.linear_model`.RidgeClassifierCV

class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, scoring=None, cv=None, class_weight=None, store_cv_values=False)

  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:
    alphas array-like 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_intercept bool, 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).

    scoring str, callable, default=None

      A string (see model evaluation documentation) or a scorer callable object / function with signature `scorer(estimator, X, y)`.

    cv int, 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_weight dict 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_values bool, 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_metadata_routing`() | Get metadata routing of this object.
  `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_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  property classes_

    Classes labels.

  decision_function(X)

    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:
      scores ndarray 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)

    Fit Ridge classifier with cv.

    Parameters:
      X ndarray 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.

      y ndarray of shape (n_samples,)

        Target values. Will be cast to X’s dtype if necessary.

      sample_weight float 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:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    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_pred ndarray 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)

    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:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RidgeClassifierCV

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RidgeClassifierCV

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.