class_docstr: Classifier using Ridge regression.
  
  Read more in the :ref:`User Guide <ridge_regression>`.
  
  Parameters
  ----------
  alpha : float
      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.
  
  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))``
  
  copy_X : boolean, optional, default True
      If True, X will be copied; else, it may be overwritten.
  
  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).
  
  max_iter : int, optional
      Maximum number of iterations for conjugate gradient solver.
      The default value is determined by scipy.sparse.linalg.
  
  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`.
  
  solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'}
      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. More stable for singular matrices than
        'cholesky'.
  
      - '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 but may not be available
        in old scipy versions. It also uses an iterative procedure.
  
      - 'sag' uses a Stochastic Average Gradient descent. It also uses an
        iterative procedure, and is faster than other solvers when both
        n_samples and n_features are large.
  
        .. versionadded:: 0.17
           Stochastic Average Gradient descent solver.
  
  tol : float
      Precision of the solution.
  
  random_state : int seed, RandomState instance, or None (default)
      The seed of the pseudo random number generator to use when
      shuffling the data. Used in 'sag' solver.
  
  Attributes
  ----------
  coef_ : array, shape (n_features,) or (n_classes, n_features)
      Weight vector(s).
  
  intercept_ : float | array, shape = (n_targets,)
      Independent term in decision function. Set to 0.0 if
      ``fit_intercept = False``.
  
  n_iter_ : array or None, shape (n_targets,)
      Actual number of iterations for each target. Available only for
      sag and lsqr solvers. Other solvers will return None.
  
  See also
  --------
  Ridge, RidgeClassifierCV
  
  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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docstr: Fit Ridge regression model.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples,n_features]
      Training data
  
  y : array-like, shape = [n_samples]
      Target values
  
  sample_weight : float or numpy array of shape (n_samples,)
      Sample weight.
  
      .. versionadded:: 0.17
         *sample_weight* support to Classifier.
  
  Returns
  -------
  self : returns an instance of self.
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code: def fit(self, X, y, sample_weight=None):
      """Fit Ridge regression model.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples,n_features]
          Training data
  
      y : array-like, shape = [n_samples]
          Target values
  
      sample_weight : float or numpy array of shape (n_samples,)
          Sample weight.
  
          .. versionadded:: 0.17
             *sample_weight* support to Classifier.
  
      Returns
      -------
      self : returns an instance of self.
      """
      self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
      Y = self._label_binarizer.fit_transform(y)
      if not self._label_binarizer.y_type_.startswith("multilabel"):
          y = column_or_1d(y, warn=True)
      else:
          # we don't (yet) support multi-label classification in Ridge
          raise ValueError(
              "%s doesn't support multi-label classification"
              % (self.__class__.__name__)
          )
  
      if self.class_weight:
          if sample_weight is None:
              sample_weight = 1.0
          # modify the sample weights with the corresponding class weight
          sample_weight = sample_weight * compute_sample_weight(
              self.class_weight, y
          )
  
      super(RidgeClassifier, self).fit(
          X, Y, sample_weight=sample_weight
      )
      return self
  
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 2 for calling the RidgeClassifier obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self