class_docstr: Epsilon-Support Vector Regression.
  
  The free parameters in the model are C and epsilon.
  
  The implementation is based on libsvm.
  
  Read more in the :ref:`User Guide <svm_regression>`.
  
  Parameters
  ----------
  C : float, optional (default=1.0)
      Penalty parameter C of the error term.
  
  epsilon : float, optional (default=0.1)
       Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
       within which no penalty is associated in the training loss function
       with points predicted within a distance epsilon from the actual
       value.
  
  kernel : string, optional (default='rbf')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
       a callable.
       If none is given, 'rbf' will be used. If a callable is given it is
       used to precompute the kernel matrix.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then 1/n_features will be used instead.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  shrinking : boolean, optional (default=True)
      Whether to use the shrinking heuristic.
  
  tol : float, optional (default=1e-3)
      Tolerance for stopping criterion.
  
  cache_size : float, optional
      Specify the size of the kernel cache (in MB).
  
  verbose : bool, default: False
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  support_vectors_ : array-like, shape = [nSV, n_features]
      Support vectors.
  
  dual_coef_ : array, shape = [1, n_SV]
      Coefficients of the support vector in the decision function.
  
  coef_ : array, shape = [1, 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 readonly property derived from `dual_coef_` and
      `support_vectors_`.
  
  intercept_ : array, shape = [1]
      Constants in decision function.
  
  sample_weight : array-like, shape = [n_samples]
          Individual weights for each sample
  
  Examples
  --------
  >>> from sklearn.svm import SVR
  >>> import numpy as np
  >>> n_samples, n_features = 10, 5
  >>> np.random.seed(0)
  >>> y = np.random.randn(n_samples)
  >>> X = np.random.randn(n_samples, n_features)
  >>> clf = SVR(C=1.0, epsilon=0.2)
  >>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
  SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto',
      kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
  
  See also
  --------
  NuSVR
      Support Vector Machine for regression implemented using libsvm
      using a parameter to control the number of support vectors.
  
  LinearSVR
      Scalable Linear Support Vector Machine for regression
      implemented using liblinear.
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code: def __init__(
      self,
      kernel="rbf",
      degree=3,
      gamma="auto",
      coef0=0.0,
      tol=1e-3,
      C=1.0,
      epsilon=0.1,
      shrinking=True,
      cache_size=200,
      verbose=False,
      max_iter=-1,
  ):
      super(SVR, self).__init__(
          "epsilon_svr",
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=C,
          nu=0.0,
          epsilon=epsilon,
          verbose=verbose,
          shrinking=shrinking,
          probability=False,
          cache_size=cache_size,
          class_weight=None,
          max_iter=max_iter,
          random_state=None,
      )
  
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 SVR object from inherited base classes: BaseLibSVM, RegressorMixin with example: obj = SVR(kernel, degree, gamma, coef0, tol, C, epsilon, shrinking, cache_size, verbose, max_iter)