class_docstr: Provide an abstract interface for classification algorithms in
  auto-sklearn.
  
  See :ref:`extending` for more information.
class_docstr_tok: ["Provide","an","abstract","interface","for","classification","algorithms","in","auto-sklearn",".","See",":","ref",":","`","extending","`","for","more","information","."]
docstr: Predict probabilities.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
  
  Returns
  -------
  array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
docstr_tok: ["Predict","probabilities",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","(","n_samples",",","n_features",")","Returns","--","--","--","-","array",",","shape=","(","n_samples",",",")","if","n_classes","==","2","else","(","n_samples",",","n_classes",")"]
code: def predict_proba(self, X):
      """Predict probabilities.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
  
      Returns
      -------
      array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
      """
      raise NotImplementedError()
  
imports: from typing import Dict, Optional
  import importlib
  import inspect
  import pkgutil
  import sys
  from collections import OrderedDict
  from sklearn.base import BaseEstimator, TransformerMixin
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.constants import SPARSE
label_desc: Use image node_id 3 for calling the AutoSklearnClassificationAlgorithm obj's underlying member method code with example usage: obj.predict_proba(X) without return types