class_docstr: Estimator for learning linear regressors by SGD.
  
  Parameters
  ----------
  loss : str, 'squared', 'epsilon_insensitive', 'huber'
      Loss function to be used.
  
  penalty : str, 'l2', 'l1', 'l1/l2'
      The penalty to be used.
  
      - l2: ridge
      - l1: lasso
      - l1/l2: group lasso
  
  alpha : float
      Weight of the penalty term.
  
  learning_rate : 'pegasos', 'constant', 'invscaling'
      Learning schedule to use.
  
  eta0 : float
      Step size.
  
  power_t : float
      Power to be used (when learning_rate='invscaling').
  
  epsilon : float
      Value to be used for epsilon-insensitive loss.
  
  fit_intercept : bool
      Whether to fit the intercept or not.
  
  intercept_decay : float
      Value by which the intercept is multiplied (to regularize it).
  
  max_iter : int
      Maximum number of iterations to perform.
  
  shuffle : bool
      Whether to shuffle data.
  
  callback : callable
      Callback function.
  
  n_calls : int
      Frequency with which `callback` must be called.
  
  random_state : RandomState or int
      The seed of the pseudo random number generator to use.
  
  verbose : int
      Verbosity level.
code: def __init__(
      self,
      loss="squared",
      penalty="l2",
      alpha=0.01,
      learning_rate="pegasos",
      eta0=0.03,
      power_t=0.5,
      epsilon=0.01,
      fit_intercept=True,
      intercept_decay=1.0,
      max_iter=10,
      shuffle=True,
      random_state=None,
      callback=None,
      n_calls=100,
      verbose=0,
  ):
      self.loss = loss
      self.penalty = penalty
      self.alpha = alpha
      self.learning_rate = learning_rate
      self.eta0 = eta0
      self.power_t = power_t
      self.epsilon = epsilon
      self.fit_intercept = fit_intercept
      self.intercept_decay = intercept_decay
      self.max_iter = max_iter
      self.shuffle = shuffle
      self.random_state = random_state
      self.callback = callback
      self.n_calls = n_calls
      self.verbose = verbose
      self.coef_ = None
  
imports: import warnings
  import numpy
  from sklearn.utils import check_random_state
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.utils.validation import assert_all_finite
  from .base import BaseClassifier
  from .base import BaseRegressor
  from .dataset_fast import get_dataset
  from .sgd_fast import _binary_sgd
  from .sgd_fast import _multiclass_sgd
  from .sgd_fast import ModifiedHuber
  from .sgd_fast import Hinge
  from .sgd_fast import SquaredHinge
  from .sgd_fast import Log
  from .sgd_fast import SquaredLoss
  from .sgd_fast import Huber
  from .sgd_fast import EpsilonInsensitive
  from .sgd_fast import MulticlassLog
  from .sgd_fast import MulticlassHinge
  from .sgd_fast import MulticlassSquaredHinge
label_desc: Use image node_id 1 to create a new SGDRegressor object from inherited base classes: BaseRegressor, _BaseSGD with example: obj = SGDRegressor(loss, penalty, alpha, learning_rate, eta0, power_t, epsilon, fit_intercept, intercept_decay, max_iter, shuffle, random_state, callback, n_calls, verbose)