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 log-probabilities for X.
  
  The predicted class log-probabilities of an input sample is computed as
  the log of the mean predicted class probabilities of the trees in the
  forest.
  
  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_log_proba(self, X):
      """Predict class log-probabilities for X.
  
      The predicted class log-probabilities of an input sample is computed as
      the log of the mean predicted class probabilities of the trees in the
      forest.
  
      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_`.
      """
      proba = self.predict_proba(X)
  
      if self.n_outputs_ == 1:
          return np.log(proba)
  
      else:
          for k in range(self.n_outputs_):
              proba[k] = np.log(proba[k])
  
          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 6 for calling the ForestClassifier obj's underlying member method code with example usage: obj.predict_log_proba(X) and returns: np, proba