class_docstr: Base class for estimators that use libsvm as backing library
  
  This implements support vector machine classification and regression.
  
  Parameter documentation is in the derived `SVC` class.
class_docstr_tok: ["Base","class","for","estimators","that","use","libsvm","as","backing","library","This","implements","support","vector","machine","classification","and","regression",".","Parameter","documentation","is","in","the","derived","`","SVC","`","class","."]
code: def __init__(
      self,
      impl,
      kernel,
      degree,
      gamma,
      coef0,
      tol,
      C,
      nu,
      epsilon,
      shrinking,
      probability,
      cache_size,
      class_weight,
      verbose,
      max_iter,
      random_state,
  ):
      if impl not in LIBSVM_IMPL:  # pragma: no cover
          raise ValueError(
              "impl should be one of %s, %s was given"
              % (LIBSVM_IMPL, impl)
          )
  
      if gamma == 0:
          msg = (
              "The gamma value of 0.0 is invalid. Use 'auto' to set"
              " gamma to a value of 1 / n_features."
          )
          raise ValueError(msg)
  
      self._impl = impl
      self.kernel = kernel
      self.degree = degree
      self.gamma = gamma
      self.coef0 = coef0
      self.tol = tol
      self.C = C
      self.nu = nu
      self.epsilon = epsilon
      self.shrinking = shrinking
      self.probability = probability
      self.cache_size = cache_size
      self.class_weight = class_weight
      self.verbose = verbose
      self.max_iter = max_iter
      self.random_state = random_state
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 1 to create a new BaseLibSVM object from inherited base classes:  with example: obj = BaseLibSVM(impl, kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose, max_iter, random_state)