class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Parameter learned in Platt scaling when `probability=True`.
  
  Returns
  -------
  ndarray of shape  (n_classes * (n_classes - 1) / 2)
code: def probA_(self):
      """Parameter learned in Platt scaling when `probability=True`.
  
      Returns
      -------
      ndarray of shape  (n_classes * (n_classes - 1) / 2)
      """
      return self._probA
  
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 11 for calling the BaseSVC obj's underlying member method code with example usage: obj.probA_() and returns: self