class_docstr: Linear least squares with l2 regularization.
  
  This model solves a regression model where the loss function is
  the linear least squares function and regularization is given by
  the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
  This estimator has built-in support for multi-variate regression
  (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
  
  Read more in the :ref:`User Guide <ridge_regression>`.
  
  Parameters
  ----------
  alpha : {float, array-like}, shape (n_targets)
      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. If an array is passed, penalties are
      assumed to be specific to the targets. Hence they must correspond in
      number.
  
  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.
      For 'sparse_cg' and 'lsqr' solvers, the default value is determined
      by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.
  
  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 often faster than other solvers when
        both n_samples and n_features are large. Note that 'sag' fast
        convergence is only guaranteed on features with approximately the
        same scale. You can preprocess the data with a scaler from
        sklearn.preprocessing.
  
      All last four solvers support both dense and sparse data. However,
      only 'sag' supports sparse input when `fit_intercept` is True.
  
      .. 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 only in 'sag' solver.
  
      .. versionadded:: 0.17
         *random_state* to support Stochastic Average Gradient.
  
  Attributes
  ----------
  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``.
  
  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.
  
      .. versionadded:: 0.17
  
  See also
  --------
  RidgeClassifier, RidgeCV, :class:`sklearn.kernel_ridge.KernelRidge`
  
  Examples
  --------
  >>> from sklearn.linear_model import Ridge
  >>> import numpy as np
  >>> n_samples, n_features = 10, 5
  >>> np.random.seed(0)
  >>> y = np.random.randn(n_samples)
  >>> X = np.random.randn(n_samples, n_features)
  >>> clf = Ridge(alpha=1.0)
  >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
  Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
        normalize=False, random_state=None, solver='auto', tol=0.001)
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code: def __init__(
      self,
      alpha=1.0,
      fit_intercept=True,
      normalize=False,
      copy_X=True,
      max_iter=None,
      tol=1e-3,
      solver="auto",
      random_state=None,
  ):
      super(Ridge, self).__init__(
          alpha=alpha,
          fit_intercept=fit_intercept,
          normalize=normalize,
          copy_X=copy_X,
          max_iter=max_iter,
          tol=tol,
          solver=solver,
          random_state=random_state,
      )
  
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 Ridge object from inherited base classes: _BaseRidge, RegressorMixin with example: obj = Ridge(alpha, fit_intercept, normalize, copy_X, max_iter, tol, solver, random_state)