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,
      kernel,
      degree,
      gamma,
      coef0,
      tol,
      C,
      nu,
      epsilon,
      shrinking,
      probability,
      cache_size,
      class_weight,
      verbose,
      max_iter,
      random_state,
  ):
      if self._impl not in LIBSVM_IMPL:
          raise ValueError(
              "impl should be one of %s, %s was given"
              % (LIBSVM_IMPL, self._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: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassifierMixin, _fit_context
  from ..exceptions import ConvergenceWarning, NotFittedError
  from ..preprocessing import LabelEncoder
  from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _ovr_decision_function, check_classification_targets
  from ..utils.validation import _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from .None import _liblinear
  from .None import _libsvm
  from .None import _libsvm_sparse
label_desc: Use image node_id 1 to create a new BaseLibSVM object from inherited base classes: BaseEstimator with example: obj = BaseLibSVM(kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose, max_iter, random_state)