class_docstr: Estimator for learning linear regressors by (proximal) SDCA.
  
  Solves the following objective:
  
  .. code-block::
  
      minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
                  + alpha * l1_ratio * ||w||_1
                  + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2
  
  Parameters
  ----------
  loss: string, {'squared', 'absolute'}
       Loss function to use in the model.
  alpha: float
       Amount of regularization (see model formulation above).
  l1_ratio: float
       Ratio between the L1 and L2 regularization (see model formulation above).
  tol : float
      stopping criterion tolerance.
  max_iter : int
      maximum number of outer iterations (also known as epochs).
  verbose : int
      verbosity level. Set positive to print progress information.
  callback : callable or None
      if given, callback(self) will be called on each outer iteration
      (epoch).
  random_state: int or RandomState
      Pseudo-random number generator state used for random sampling.
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code: def __init__(
      self,
      alpha=1.0,
      l1_ratio=0,
      loss="squared",
      max_iter=100,
      tol=1e-3,
      callback=None,
      n_calls=None,
      verbose=0,
      random_state=None,
  ):
      self.alpha = alpha
      self.l1_ratio = l1_ratio
      self.loss = loss
      self.gamma = 1.0
      self.max_iter = max_iter
      self.tol = tol
      self.callback = callback
      self.n_calls = n_calls
      self.verbose = verbose
      self.random_state = random_state
  
imports: import numpy
  from sklearn.utils import check_random_state
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .sdca_fast import _prox_sdca_fit
label_desc: Use image node_id 1 to create a new SDCARegressor object from inherited base classes: BaseRegressor, _BaseSDCA with example: obj = SDCARegressor(alpha, l1_ratio, loss, max_iter, tol, callback, n_calls, verbose, random_state)