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# `sklearn.linear_model`.RidgeClassifier

class sklearn.linear_model.RidgeClassifier(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, class_weight=None, solver='auto', positive=False, random_state=None)

  Classifier using Ridge regression.

  This classifier first converts the target values into `{-1, 1}` and then treats the problem as a regression task (multi-output regression in the multiclass case).

  Read more in the User Guide.

  Parameters:
    alpha float, default=1.0

      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 (e.g. data is expected to be already centered).

    copy_X bool, default=True

      If True, X will be copied; else, it may be overwritten.

    max_iter int, default=None

      Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.

    tol float, default=1e-4

      The precision of the solution (`coef_`) is determined by `tol` which specifies a different convergence criterion for each solver:

      - ‘svd’: `tol` has no impact.

      - ‘cholesky’: `tol` has no impact.

      - ‘sparse_cg’: norm of residuals smaller than `tol`.

      - ‘lsqr’: `tol` is set as atol and btol of scipy.sparse.linalg.lsqr, which control the norm of the residual vector in terms of the norms of matrix and coefficients.

      - ‘sag’ and ‘saga’: relative change of coef smaller than `tol`.

      - ‘lbfgs’: maximum of the absolute (projected) gradient=max|residuals| smaller than `tol`.

      Changed in version 1.2: Default value changed from 1e-3 to 1e-4 for consistency with other linear models.

    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))`.

    solver{‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’

      Solver to use in the computational routines:

      - ‘auto’ chooses the solver automatically based on the type of data.

      - ‘svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. It is the most stable solver, in particular more stable for singular matrices than ‘cholesky’ at the cost of being slower.

      - ‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closed-form solution.

      - ‘sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for large-scale data (possibility to set `tol` and `max_iter`).

      - ‘lsqr’ uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure.

      - ‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.

        New in version 0.17: Stochastic Average Gradient descent solver.

        New in version 0.19: SAGA solver.

      - ‘lbfgs’ uses L-BFGS-B algorithm implemented in `scipy.optimize.minimize`. It can be used only when `positive` is True.

    positive bool, default=False

      When set to `True`, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case.

    random_state int, RandomState instance, default=None

      Used when `solver` == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.

  Attributes:
    coef_ndarray of shape (1, n_features) or (n_classes, 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`.

    n_iter_None or ndarray of shape (n_targets,)

      Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.

    `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.

  `RidgeClassifierCV`

    Ridge classifier 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 RidgeClassifier
      >>> X, y = load_breast_cancer(return_X_y=True)
      >>> clf = RidgeClassifier().fit(X, y)
      >>> clf.score(X, y)
      0.9595...

  Methods

  `decision_function`(X) | Predict confidence scores for samples.
  `fit`(X, y[, sample_weight]) | Fit Ridge classifier model.
  `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 model.

    Parameters:
      X{ndarray, sparse matrix} of shape (n_samples, n_features)

        Training data.

      y ndarray of shape (n_samples,)

        Target values.

      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.

        New in version 0.17: sample_weight support to RidgeClassifier.

    Returns:
      self object

        Instance of the 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 = '$UNCHANGED$') → RidgeClassifier

    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 = '$UNCHANGED$') → RidgeClassifier

    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.

## Examples using `sklearn.linear_model.RidgeClassifier`

Classification of text documents using sparse features

Classification of text documents using sparse features