class_docstr: Base class for forest of trees-based classifiers.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","forest","of","trees-based","classifiers",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: Predict class probabilities for X.
  
  The predicted class probabilities of an input sample are computed as
  the mean predicted class probabilities of the trees in the forest. The
  class probability of a single tree is the fraction of samples of the same
  class in a leaf.
  
  Parameters
  ----------
  X : array-like or sparse matrix of shape = [n_samples, n_features]
      The input samples. Internally, its dtype will be converted to
      ``dtype=np.float32``. If a sparse matrix is provided, it will be
      converted into a sparse ``csr_matrix``.
  
  Returns
  -------
  p : array of shape = [n_samples, n_classes], or a list of n_outputs
      such arrays if n_outputs > 1.
      The class probabilities of the input samples. The order of the
      classes corresponds to that in the attribute `classes_`.
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code: def predict_proba(self, X):
      """Predict class probabilities for X.
  
      The predicted class probabilities of an input sample are computed as
      the mean predicted class probabilities of the trees in the forest. The
      class probability of a single tree is the fraction of samples of the same
      class in a leaf.
  
      Parameters
      ----------
      X : array-like or sparse matrix of shape = [n_samples, n_features]
          The input samples. Internally, its dtype will be converted to
          ``dtype=np.float32``. If a sparse matrix is provided, it will be
          converted into a sparse ``csr_matrix``.
  
      Returns
      -------
      p : array of shape = [n_samples, n_classes], or a list of n_outputs
          such arrays if n_outputs > 1.
          The class probabilities of the input samples. The order of the
          classes corresponds to that in the attribute `classes_`.
      """
      # Check data
      X = self._validate_X_predict(X)
  
      # Assign chunk of trees to jobs
      n_jobs, _, _ = _partition_estimators(
          self.n_estimators, self.n_jobs
      )
  
      # Parallel loop
      all_proba = Parallel(
          n_jobs=n_jobs, verbose=self.verbose, backend="threading"
      )(
          delayed(parallel_helper)(
              e, "predict_proba", X, check_input=False
          )
          for e in self.estimators_
      )
  
      # Reduce
      proba = all_proba[0]
  
      if self.n_outputs_ == 1:
          for j in range(1, len(all_proba)):
              proba += all_proba[j]
  
          proba /= len(self.estimators_)
  
      else:
          for j in range(1, len(all_proba)):
              for k in range(self.n_outputs_):
                  proba[k] += all_proba[j][k]
  
          for k in range(self.n_outputs_):
              proba[k] /= self.n_estimators
  
      return proba
  
imports: from __future__ import division
  import warnings
  from warnings import warn
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from scipy.sparse import hstack
  from ..base import ClassifierMixin, RegressorMixin
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..metrics import r2_score
  from ..preprocessing import OneHotEncoder
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor
  from ..tree._tree import DTYPE, DOUBLE
  from ..utils import check_random_state, check_array, compute_sample_weight
  from ..exceptions import DataConversionWarning, NotFittedError
  from .base import BaseEnsemble, _partition_estimators
  from ..utils.fixes import bincount, parallel_helper
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 5 for calling the ForestClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: proba