class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def __init__(
      self,
      kernel,
      degree,
      gamma,
      coef0,
      tol,
      C,
      nu,
      shrinking,
      probability,
      cache_size,
      class_weight,
      verbose,
      max_iter,
      decision_function_shape,
      random_state,
      break_ties,
  ):
      self.decision_function_shape = decision_function_shape
      self.break_ties = break_ties
      super().__init__(
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=C,
          nu=nu,
          epsilon=0.0,
          shrinking=shrinking,
          probability=probability,
          cache_size=cache_size,
          class_weight=class_weight,
          verbose=verbose,
          max_iter=max_iter,
          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 BaseSVC object from inherited base classes: ClassifierMixin, BaseLibSVM with example: obj = BaseSVC(kernel, degree, gamma, coef0, tol, C, nu, shrinking, probability, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state, break_ties)