class_docstr: Ridge classifier with built-in cross-validation.
  
  By default, it performs Generalized Cross-Validation, which is a form of
  efficient Leave-One-Out cross-validation. Currently, only the n_features >
  n_samples case is handled efficiently.
  
  Read more in the :ref:`User Guide <ridge_regression>`.
  
  Parameters
  ----------
  alphas : numpy array of shape [n_alphas]
      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 ``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.
      This parameter is ignored when `fit_intercept` is set to False.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      `preprocessing.StandardScaler` before calling `fit` on an estimator
      with `normalize=False`.
  
  scoring : string, callable or None, optional, 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, optional
      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.
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
  class_weight : dict or 'balanced', optional
      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))``
  
  Attributes
  ----------
  cv_values_ : array, shape = [n_samples, n_alphas] or     shape = [n_samples, n_responses, 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).
  
  intercept_ : float | array, shape = (n_targets,)
      Independent term in decision function. Set to 0.0 if
      ``fit_intercept = False``.
  
  alpha_ : float
      Estimated regularization parameter
  
  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.
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code: def __init__(
      self,
      alphas=(0.1, 1.0, 10.0),
      fit_intercept=True,
      normalize=False,
      scoring=None,
      cv=None,
      class_weight=None,
  ):
      super(RidgeClassifierCV, self).__init__(
          alphas=alphas,
          fit_intercept=fit_intercept,
          normalize=normalize,
          scoring=scoring,
          cv=cv,
      )
      self.class_weight = class_weight
  
imports: from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse import linalg
  from .base import LinearClassifierMixin, LinearModel, _rescale_data
  from .sag import sag_solver
  from ..base import RegressorMixin
  from ..utils.extmath import safe_sparse_dot
  from ..utils.extmath import row_norms
  from ..utils import check_X_y
  from ..utils import check_array
  from ..utils import check_consistent_length
  from ..utils import compute_sample_weight
  from ..utils import column_or_1d
  from ..preprocessing import LabelBinarizer
  from ..model_selection import GridSearchCV
  from ..externals import six
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 1 to create a new RidgeClassifierCV object from inherited base classes: LinearClassifierMixin, _BaseRidgeCV with example: obj = RidgeClassifierCV(alphas, fit_intercept, normalize, scoring, cv, class_weight)