class_docstr: A random forest classifier.
  
  A random forest is a meta estimator that fits a number of decision tree
  classifiers on various sub-samples of the dataset and use averaging to
  improve the predictive accuracy and control over-fitting.
  The sub-sample size is always the same as the original
  input sample size but the samples are drawn with replacement if
  `bootstrap=True` (default).
  
  Read more in the :ref:`User Guide <forest>`.
  
  Parameters
  ----------
  n_estimators : integer, optional (default=10)
      The number of trees in the forest.
  
  criterion : string, optional (default="gini")
      The function to measure the quality of a split. Supported criteria are
      "gini" for the Gini impurity and "entropy" for the information gain.
      Note: this parameter is tree-specific.
  
  max_features : int, float, string or None, optional (default="auto")
      The number of features to consider when looking for the best split:
  
      - If int, then consider `max_features` features at each split.
      - If float, then `max_features` is a percentage and
        `int(max_features * n_features)` features are considered at each
        split.
      - If "auto", then `max_features=sqrt(n_features)`.
      - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
      - If "log2", then `max_features=log2(n_features)`.
      - If None, then `max_features=n_features`.
  
      Note: the search for a split does not stop until at least one
      valid partition of the node samples is found, even if it requires to
      effectively inspect more than ``max_features`` features.
  
  max_depth : integer or None, optional (default=None)
      The maximum depth of the tree. If None, then nodes are expanded until
      all leaves are pure or until all leaves contain less than
      min_samples_split samples.
  
  min_samples_split : int, float, optional (default=2)
      The minimum number of samples required to split an internal node:
  
      - If int, then consider `min_samples_split` as the minimum number.
      - If float, then `min_samples_split` is a percentage and
        `ceil(min_samples_split * n_samples)` are the minimum
        number of samples for each split.
  
      .. versionchanged:: 0.18
         Added float values for percentages.
  
  min_samples_leaf : int, float, optional (default=1)
      The minimum number of samples required to be at a leaf node:
  
      - If int, then consider `min_samples_leaf` as the minimum number.
      - If float, then `min_samples_leaf` is a percentage and
        `ceil(min_samples_leaf * n_samples)` are the minimum
        number of samples for each node.
  
      .. versionchanged:: 0.18
         Added float values for percentages.
  
  min_weight_fraction_leaf : float, optional (default=0.)
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node. Samples have
      equal weight when sample_weight is not provided.
  
  max_leaf_nodes : int or None, optional (default=None)
      Grow trees with ``max_leaf_nodes`` in best-first fashion.
      Best nodes are defined as relative reduction in impurity.
      If None then unlimited number of leaf nodes.
  
  min_impurity_split : float, optional (default=1e-7)
      Threshold for early stopping in tree growth. A node will split
      if its impurity is above the threshold, otherwise it is a leaf.
  
      .. versionadded:: 0.18
  
  bootstrap : boolean, optional (default=True)
      Whether bootstrap samples are used when building trees.
  
  oob_score : bool (default=False)
      Whether to use out-of-bag samples to estimate
      the generalization accuracy.
  
  n_jobs : integer, optional (default=1)
      The number of jobs to run in parallel for both `fit` and `predict`.
      If -1, then the number of jobs is set to the number of cores.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  verbose : int, optional (default=0)
      Controls the verbosity of the tree building process.
  
  warm_start : bool, optional (default=False)
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just fit a whole
      new forest.
  
  class_weight : dict, list of dicts, "balanced",
      "balanced_subsample" or None, optional (default=None)
      Weights associated with classes in the form ``{class_label: weight}``.
      If not given, all classes are supposed to have weight one. For
      multi-output problems, a list of dicts can be provided in the same
      order as the columns of y.
  
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
  
      The "balanced_subsample" mode is the same as "balanced" except that
      weights are computed based on the bootstrap sample for every tree
      grown.
  
      For multi-output, the weights of each column of y will be multiplied.
  
      Note that these weights will be multiplied with sample_weight (passed
      through the fit method) if sample_weight is specified.
  
  Attributes
  ----------
  estimators_ : list of DecisionTreeClassifier
      The collection of fitted sub-estimators.
  
  classes_ : array of shape = [n_classes] or a list of such arrays
      The classes labels (single output problem), or a list of arrays of
      class labels (multi-output problem).
  
  n_classes_ : int or list
      The number of classes (single output problem), or a list containing the
      number of classes for each output (multi-output problem).
  
  n_features_ : int
      The number of features when ``fit`` is performed.
  
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  
  feature_importances_ : array of shape = [n_features]
      The feature importances (the higher, the more important the feature).
  
  oob_score_ : float
      Score of the training dataset obtained using an out-of-bag estimate.
  
  oob_decision_function_ : array of shape = [n_samples, n_classes]
      Decision function computed with out-of-bag estimate on the training
      set. If n_estimators is small it might be possible that a data point
      was never left out during the bootstrap. In this case,
      `oob_decision_function_` might contain NaN.
  
  References
  ----------
  
  .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
  
  See also
  --------
  DecisionTreeClassifier, ExtraTreesClassifier
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code: def __init__(
      self,
      n_estimators=10,
      criterion="gini",
      max_depth=None,
      min_samples_split=2,
      min_samples_leaf=1,
      min_weight_fraction_leaf=0.0,
      max_features="auto",
      max_leaf_nodes=None,
      min_impurity_split=1e-7,
      bootstrap=True,
      oob_score=False,
      n_jobs=1,
      random_state=None,
      verbose=0,
      warm_start=False,
      class_weight=None,
  ):
      super(RandomForestClassifier, self).__init__(
          base_estimator=DecisionTreeClassifier(),
          n_estimators=n_estimators,
          estimator_params=(
              "criterion",
              "max_depth",
              "min_samples_split",
              "min_samples_leaf",
              "min_weight_fraction_leaf",
              "max_features",
              "max_leaf_nodes",
              "min_impurity_split",
              "random_state",
          ),
          bootstrap=bootstrap,
          oob_score=oob_score,
          n_jobs=n_jobs,
          random_state=random_state,
          verbose=verbose,
          warm_start=warm_start,
          class_weight=class_weight,
      )
  
      self.criterion = criterion
      self.max_depth = max_depth
      self.min_samples_split = min_samples_split
      self.min_samples_leaf = min_samples_leaf
      self.min_weight_fraction_leaf = min_weight_fraction_leaf
      self.max_features = max_features
      self.max_leaf_nodes = max_leaf_nodes
      self.min_impurity_split = min_impurity_split
  
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 1 to create a new RandomForestClassifier object from inherited base classes: ForestClassifier with example: obj = RandomForestClassifier(n_estimators, criterion, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, min_impurity_split, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, class_weight)