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.
  
  The main differences between :class:`~sklearn.svm.LinearSVR` and
  :class:`~sklearn.svm.SVR` lie in the loss function used by default, and in
  the handling of intercept regularization between those two implementations.
  
  This class supports both dense and sparse input.
  
  Read more in the :ref:`User Guide <svm_regression>`.
  
  .. versionadded:: 0.16
  
  Parameters
  ----------
  epsilon : float, default=0.0
      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``.
  
  tol : float, default=1e-4
      Tolerance for stopping criteria.
  
  C : float, default=1.0
      Regularization parameter. The strength of the regularization is
      inversely proportional to C. Must be strictly positive.
  
  loss : {'epsilon_insensitive', 'squared_epsilon_insensitive'},             default='epsilon_insensitive'
      Specifies the loss function. The epsilon-insensitive loss
      (standard SVR) is the L1 loss, while the squared epsilon-insensitive
      loss ('squared_epsilon_insensitive') is the L2 loss.
  
  fit_intercept : bool, default=True
      Whether or not to fit an intercept. If set to True, the feature vector
      is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where
      1 corresponds to the intercept. If set to False, no intercept will be
      used in calculations (i.e. data is expected to be already centered).
  
  intercept_scaling : float, default=1.0
      When `fit_intercept` is True, the instance vector x becomes `[x_1, ...,
      x_n, intercept_scaling]`, i.e. a "synthetic" feature with a constant
      value equal to `intercept_scaling` is appended to the instance vector.
      The intercept becomes intercept_scaling * synthetic feature weight.
      Note that liblinear internally penalizes the intercept, treating it
      like any other term in the feature vector. To reduce the impact of the
      regularization on the intercept, the `intercept_scaling` parameter can
      be set to a value greater than 1; the higher the value of
      `intercept_scaling`, the lower the impact of regularization on it.
      Then, the weights become `[w_x_1, ..., w_x_n,
      w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent
      the feature weights and the intercept weight is scaled by
      `intercept_scaling`. This scaling allows the intercept term to have a
      different regularization behavior compared to the other features.
  
  dual : "auto" or bool, default=True
      Select the algorithm to either solve the dual or primal
      optimization problem. Prefer dual=False when n_samples > n_features.
      `dual="auto"` will choose the value of the parameter automatically,
      based on the values of `n_samples`, `n_features` and `loss`. If
      `n_samples` < `n_features` and optimizer supports chosen `loss`,
      then dual will be set to True, otherwise it will be set to False.
  
      .. versionchanged:: 1.3
         The `"auto"` option is added in version 1.3 and will be the default
         in version 1.5.
  
  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, RandomState instance or None, default=None
      Controls the pseudo random number generation for shuffling the data.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  max_iter : int, default=1000
      The maximum number of iterations to be run.
  
  Attributes
  ----------
  coef_ : ndarray of shape (n_features) if n_classes == 2             else (n_classes, n_features)
      Weights assigned to the features (coefficients in the primal
      problem).
  
      `coef_` is a readonly property derived from `raw_coef_` that
      follows the internal memory layout of liblinear.
  
  intercept_ : ndarray of shape (1) if n_classes == 2 else (n_classes)
      Constants in decision function.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_iter_ : int
      Maximum number of iterations run across all classes.
  
  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 :class:`~sklearn.svm.LinearSVR` 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.
  
  Examples
  --------
  >>> from sklearn.svm import LinearSVR
  >>> from sklearn.pipeline import make_pipeline
  >>> from sklearn.preprocessing import StandardScaler
  >>> from sklearn.datasets import make_regression
  >>> X, y = make_regression(n_features=4, random_state=0)
  >>> regr = make_pipeline(StandardScaler(),
  ...                      LinearSVR(dual="auto", random_state=0, tol=1e-5))
  >>> regr.fit(X, y)
  Pipeline(steps=[('standardscaler', StandardScaler()),
                  ('linearsvr', LinearSVR(dual='auto', random_state=0, tol=1e-05))])
  
  >>> print(regr.named_steps['linearsvr'].coef_)
  [18.582... 27.023... 44.357... 64.522...]
  >>> print(regr.named_steps['linearsvr'].intercept_)
  [-4...]
  >>> print(regr.predict([[0, 0, 0, 0]]))
  [-2.384...]
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docstr: Fit the model according to the given training data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training vector, where `n_samples` is the number of samples and
      `n_features` is the number of features.
  
  y : array-like of shape (n_samples,)
      Target vector relative to X.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Array of weights that are assigned to individual
      samples. If not provided,
      then each sample is given unit weight.
  
      .. versionadded:: 0.18
  
  Returns
  -------
  self : object
      An instance of the estimator.
code: def fit(self, X, y, sample_weight=None):
      """Fit the model according to the given training data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vector, where `n_samples` is the number of samples and
          `n_features` is the number of features.
  
      y : array-like of shape (n_samples,)
          Target vector relative to X.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Array of weights that are assigned to individual
          samples. If not provided,
          then each sample is given unit weight.
  
          .. versionadded:: 0.18
  
      Returns
      -------
      self : object
          An instance of the estimator.
      """
      X, y = self._validate_data(
          X,
          y,
          accept_sparse="csr",
          dtype=np.float64,
          order="C",
          accept_large_sparse=False,
      )
      penalty = "l2"  # SVR only accepts l2 penalty
  
      _dual = _validate_dual_parameter(
          self.dual, self.loss, penalty, "ovr", X
      )
  
      self.coef_, self.intercept_, n_iter_ = _fit_liblinear(
          X,
          y,
          self.C,
          self.fit_intercept,
          self.intercept_scaling,
          None,
          penalty,
          _dual,
          self.verbose,
          self.max_iter,
          self.tol,
          self.random_state,
          loss=self.loss,
          epsilon=self.epsilon,
          sample_weight=sample_weight,
      )
      self.coef_ = self.coef_.ravel()
      # Backward compatibility: _fit_liblinear is used both by LinearSVC/R
      # and LogisticRegression but LogisticRegression sets a structured
      # `n_iter_` attribute with information about the underlying OvR fits
      # while LinearSVC/R only reports the maximum value.
      self.n_iter_ = n_iter_.max().item()
  
      return self
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
  from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
  from ..utils import deprecated
  from ..utils._param_validation import Hidden, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _num_samples
  from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
label_desc: Use image node_id 2 for calling the LinearSVR obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self