class_docstr: Linear Support Vector Regression.
  
  Similar to SVR with parameter kernel='linear', but implemented in terms of
  liblinear rather than libsvm, so it has more flexibility in the choice of
  penalties and loss functions and should scale better to large numbers of
  samples.
  
  This class supports both dense and sparse input.
  
  Read more in the :ref:`User Guide <svm_regression>`.
  
  Parameters
  ----------
  C : float, optional (default=1.0)
      Penalty parameter C of the error term. The penalty is a squared
      l2 penalty. The bigger this parameter, the less regularization is used.
  
  loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' (default='epsilon_insensitive')
      Specifies the loss function. 'l1' is the epsilon-insensitive loss
      (standard SVR) while 'l2' is the squared epsilon-insensitive loss.
  
  epsilon : float, optional (default=0.1)
      Epsilon parameter in the epsilon-insensitive loss function. Note
      that the value of this parameter depends on the scale of the target
      variable y. If unsure, set ``epsilon=0``.
  
  dual : bool, (default=True)
      Select the algorithm to either solve the dual or primal
      optimization problem. Prefer dual=False when n_samples > n_features.
  
  tol : float, optional (default=1e-4)
      Tolerance for stopping criteria.
  
  fit_intercept : boolean, optional (default=True)
      Whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (i.e. data is expected to be already centered).
  
  intercept_scaling : float, optional (default=1)
      When self.fit_intercept is True, instance vector x becomes
      [x, self.intercept_scaling],
      i.e. a "synthetic" feature with constant value equals to
      intercept_scaling is appended to the instance vector.
      The intercept becomes intercept_scaling * synthetic feature weight
      Note! the synthetic feature weight is subject to l1/l2 regularization
      as all other features.
      To lessen the effect of regularization on synthetic feature weight
      (and therefore on the intercept) intercept_scaling has to be increased.
  
  verbose : int, (default=0)
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in liblinear that, if enabled, may not work
      properly in a multithreaded context.
  
  random_state : int seed, RandomState instance, or None (default=None)
      The seed of the pseudo random number generator to use when
      shuffling the data.
  
  max_iter : int, (default=1000)
      The maximum number of iterations to be run.
  
  Attributes
  ----------
  coef_ : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
  
      `coef_` is a readonly property derived from `raw_coef_` that
      follows the internal memory layout of liblinear.
  
  intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
      Constants in decision function.
  
  
  See also
  --------
  LinearSVC
      Implementation of Support Vector Machine classifier using the
      same library as this class (liblinear).
  
  SVR
      Implementation of Support Vector Machine regression using libsvm:
      the kernel can be non-linear but its SMO algorithm does not
      scale to large number of samples as LinearSVC does.
  
  sklearn.linear_model.SGDRegressor
      SGDRegressor can optimize the same cost function as LinearSVR
      by adjusting the penalty and loss parameters. In addition it requires
      less memory, allows incremental (online) learning, and implements
      various loss functions and regularization regimes.
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code: def __init__(
      self,
      epsilon=0.0,
      tol=1e-4,
      C=1.0,
      loss="epsilon_insensitive",
      fit_intercept=True,
      intercept_scaling=1.0,
      dual=True,
      verbose=0,
      random_state=None,
      max_iter=1000,
  ):
      self.tol = tol
      self.C = C
      self.epsilon = epsilon
      self.fit_intercept = fit_intercept
      self.intercept_scaling = intercept_scaling
      self.verbose = verbose
      self.random_state = random_state
      self.max_iter = max_iter
      self.dual = dual
      self.loss = loss
  
imports: import warnings
  import numpy
  from .base import _fit_liblinear, BaseSVC, BaseLibSVM
  from ..base import BaseEstimator, RegressorMixin
  from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, LinearModel
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..utils import check_X_y
  from ..utils.validation import _num_samples
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 1 to create a new LinearSVR object from inherited base classes: LinearModel, RegressorMixin with example: obj = LinearSVR(epsilon, tol, C, loss, fit_intercept, intercept_scaling, dual, verbose, random_state, max_iter)