class_docstr: Epsilon--Support Vector Regression.
class_docstr_tok: ["Epsilon","--","Support","Vector","Regression","."]
code: def __init__(
      self,
      C=1.0,
      epsilon=0.1,
      kernel="rbf",
      *,
      degree=3,
      gamma="scale",
      coef0=0.0,
      tol=1e-3,
      shrinking=True,
      cache_size=200.0,
      max_iter=-1,
      tau=1e-12,
      algorithm="thunder",
      **kwargs,
  ):
      super().__init__(
          C=C,
          nu=0.5,
          epsilon=epsilon,
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          shrinking=shrinking,
          cache_size=cache_size,
          max_iter=max_iter,
          tau=tau,
          class_weight=None,
          decision_function_shape=None,
          break_ties=False,
          algorithm=algorithm,
      )
      self.svm_type = SVMtype.epsilon_svr
  
imports: from abc import ABCMeta, abstractmethod
  from enum import Enum
  from numbers import Number, Real
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 1 to create a new SVR object from inherited base classes: RegressorMixin, BaseSVM with example: obj = SVR(C, epsilon, kernel)