class_docstr: Linear regression model that is robust to outliers.
  
  The Huber Regressor optimizes the squared loss for the samples where
  ``|(y - X'w) / sigma| < epsilon`` and the absolute loss for the samples
  where ``|(y - X'w) / sigma| > epsilon``, where w and sigma are parameters
  to be optimized. The parameter sigma makes sure that if y is scaled up
  or down by a certain factor, one does not need to rescale epsilon to
  achieve the same robustness. Note that this does not take into account
  the fact that the different features of X may be of different scales.
  
  This makes sure that the loss function is not heavily influenced by the
  outliers while not completely ignoring their effect.
  
  Read more in the :ref:`User Guide <huber_regression>`
  
  .. versionadded:: 0.18
  
  Parameters
  ----------
  epsilon : float, greater than 1.0, default 1.35
      The parameter epsilon controls the number of samples that should be
      classified as outliers. The smaller the epsilon, the more robust it is
      to outliers.
  
  max_iter : int, default 100
      Maximum number of iterations that scipy.optimize.fmin_l_bfgs_b
      should run for.
  
  alpha : float, default 0.0001
      Regularization parameter.
  
  warm_start : bool, default False
      This is useful if the stored attributes of a previously used model
      has to be reused. If set to False, then the coefficients will
      be rewritten for every call to fit.
  
  fit_intercept : bool, default True
      Whether or not to fit the intercept. This can be set to False
      if the data is already centered around the origin.
  
  tol : float, default 1e-5
      The iteration will stop when
      ``max{|proj g_i | i = 1, ..., n}`` <= ``tol``
      where pg_i is the i-th component of the projected gradient.
  
  Attributes
  ----------
  coef_ : array, shape (n_features,)
      Features got by optimizing the Huber loss.
  
  intercept_ : float
      Bias.
  
  scale_ : float
      The value by which ``|y - X'w - c|`` is scaled down.
  
  n_iter_ : int
      Number of iterations that fmin_l_bfgs_b has run for.
      Not available if SciPy version is 0.9 and below.
  
  outliers_: array, shape (n_samples,)
      A boolean mask which is set to True where the samples are identified
      as outliers.
  
  References
  ----------
  .. [1] Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics
         Concomitant scale estimates, pg 172
  .. [2] Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
         http://statweb.stanford.edu/~owen/reports/hhu.pdf
code: def __init__(
      self,
      epsilon=1.35,
      max_iter=100,
      alpha=0.0001,
      warm_start=False,
      fit_intercept=True,
      tol=1e-05,
  ):
      self.epsilon = epsilon
      self.max_iter = max_iter
      self.alpha = alpha
      self.warm_start = warm_start
      self.fit_intercept = fit_intercept
      self.tol = tol
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/linear_model/huber.py
imports: import numpy
  from scipy import optimize, sparse
  from ..base import BaseEstimator, RegressorMixin
  from .base import LinearModel
  from ..utils import check_X_y
  from ..utils import check_consistent_length
  from ..utils import axis0_safe_slice
  from ..utils.extmath import safe_sparse_dot
label_desc: Use image node_id 1 to create a new HuberRegressor object from inherited base classes: LinearModel, RegressorMixin, BaseEstimator with example: obj = HuberRegressor(epsilon, max_iter, alpha, warm_start, fit_intercept, tol)