class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Compute probabilities of possible outcomes for samples in X.
  
  The model needs to have probability information computed at training
  time: fit with attribute `probability` set to True.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      For kernel="precomputed", the expected shape of X is
      (n_samples_test, n_samples_train).
  
  Returns
  -------
  T : ndarray of shape (n_samples, n_classes)
      Returns the probability of the sample for each class in
      the model. The columns correspond to the classes in sorted
      order, as they appear in the attribute :term:`classes_`.
  
  Notes
  -----
  The probability model is created using cross validation, so
  the results can be slightly different than those obtained by
  predict. Also, it will produce meaningless results on very small
  datasets.
code: def predict_proba(self, X):
      """Compute probabilities of possible outcomes for samples in X.
  
      The model needs to have probability information computed at training
      time: fit with attribute `probability` set to True.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          (n_samples_test, n_samples_train).
  
      Returns
      -------
      T : ndarray of shape (n_samples, n_classes)
          Returns the probability of the sample for each class in
          the model. The columns correspond to the classes in sorted
          order, as they appear in the attribute :term:`classes_`.
  
      Notes
      -----
      The probability model is created using cross validation, so
      the results can be slightly different than those obtained by
      predict. Also, it will produce meaningless results on very small
      datasets.
      """
      X = self._validate_for_predict(X)
      if self.probA_.size == 0 or self.probB_.size == 0:
          raise NotFittedError(
              "predict_proba is not available when fitted with probability=False"
          )
      pred_proba = (
          self._sparse_predict_proba
          if self._sparse
          else self._dense_predict_proba
      )
      return pred_proba(X)
  
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 6 for calling the BaseSVC obj's underlying member method code with example usage: obj.predict_proba(X) and returns: pred_proba