class_docstr: Epsilon-Support Vector Regression.
  
  The free parameters in the model are C and epsilon.
  
  The implementation is based on libsvm. The fit time complexity
  is more than quadratic with the number of samples which makes it hard
  to scale to datasets with more than a couple of 10000 samples. For large
  datasets consider using :class:`~sklearn.svm.LinearSVR` or
  :class:`~sklearn.linear_model.SGDRegressor` instead, possibly after a
  :class:`~sklearn.kernel_approximation.Nystroem` transformer or
  other :ref:`kernel_approximation`.
  
  Read more in the :ref:`User Guide <svm_regression>`.
  
  Parameters
  ----------
  kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'
       Specifies the kernel type to be used in the algorithm.
       If none is given, 'rbf' will be used. If a callable is given it is
       used to precompute the kernel matrix.
  
  degree : int, default=3
      Degree of the polynomial kernel function ('poly').
      Must be non-negative. Ignored by all other kernels.
  
  gamma : {'scale', 'auto'} or float, default='scale'
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
  
      - if ``gamma='scale'`` (default) is passed then it uses
        1 / (n_features * X.var()) as value of gamma,
      - if 'auto', uses 1 / n_features
      - if float, must be non-negative.
  
      .. versionchanged:: 0.22
         The default value of ``gamma`` changed from 'auto' to 'scale'.
  
  coef0 : float, default=0.0
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  tol : float, default=1e-3
      Tolerance for stopping criterion.
  
  C : float, default=1.0
      Regularization parameter. The strength of the regularization is
      inversely proportional to C. Must be strictly positive.
      The penalty is a squared l2 penalty.
  
  epsilon : float, 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. Must be non-negative.
  
  shrinking : bool, default=True
      Whether to use the shrinking heuristic.
      See the :ref:`User Guide <shrinking_svm>`.
  
  cache_size : float, default=200
      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, default=-1
      Hard limit on iterations within solver, or -1 for no limit.
  
  Attributes
  ----------
  class_weight_ : ndarray of shape (n_classes,)
      Multipliers of parameter C for each class.
      Computed based on the ``class_weight`` parameter.
  
      .. deprecated:: 1.2
          `class_weight_` was deprecated in version 1.2 and will be removed in 1.4.
  
  coef_ : ndarray of 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_`.
  
  dual_coef_ : ndarray of shape (1, n_SV)
      Coefficients of the support vector in the decision function.
  
  fit_status_ : int
      0 if correctly fitted, 1 otherwise (will raise warning)
  
  intercept_ : ndarray of shape (1,)
      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
      Number of iterations run by the optimization routine to fit the model.
  
      .. versionadded:: 1.1
  
  n_support_ : ndarray of shape (1,), dtype=int32
      Number of support vectors.
  
  shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
      Array dimensions of training vector ``X``.
  
  support_ : ndarray of shape (n_SV,)
      Indices of support vectors.
  
  support_vectors_ : ndarray of shape (n_SV, n_features)
      Support vectors.
  
  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.
  
  References
  ----------
  .. [1] `LIBSVM: A Library for Support Vector Machines
      <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
  
  .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
      Machines and Comparisons to Regularized Likelihood Methods"
      <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
  
  Examples
  --------
  >>> from sklearn.svm import SVR
  >>> from sklearn.pipeline import make_pipeline
  >>> from sklearn.preprocessing import StandardScaler
  >>> import numpy as np
  >>> n_samples, n_features = 10, 5
  >>> rng = np.random.RandomState(0)
  >>> y = rng.randn(n_samples)
  >>> X = rng.randn(n_samples, n_features)
  >>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
  >>> regr.fit(X, y)
  Pipeline(steps=[('standardscaler', StandardScaler()),
                  ('svr', SVR(epsilon=0.2))])
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code: def _more_tags(self):
      return {
          "_xfail_checks": {
              "check_sample_weights_invariance": (
                  "zero sample_weight is not equivalent to removing samples"
              ),
          }
      }
  
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 3 for calling the SVR obj's underlying member method code with example usage: obj._more_tags() and returns: dict