class_docstr: An extremely randomized tree classifier.
  
  Extra-trees differ from classic decision trees in the way they are built.
  When looking for the best split to separate the samples of a node into two
  groups, random splits are drawn for each of the `max_features` randomly
  selected features and the best split among those is chosen. When
  `max_features` is set 1, this amounts to building a totally random
  decision tree.
  
  Warning: Extra-trees should only be used within ensemble methods.
  
  Read more in the :ref:`User Guide <tree>`.
  
  See also
  --------
  ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  
  References
  ----------
  
  .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
         Machine Learning, 63(1), 3-42, 2006.
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code: def __init__(
      self,
      criterion="gini",
      splitter="random",
      max_depth=None,
      min_samples_split=2,
      min_samples_leaf=1,
      min_weight_fraction_leaf=0.0,
      max_features="auto",
      random_state=None,
      max_leaf_nodes=None,
      min_impurity_split=1e-7,
      class_weight=None,
  ):
      super(ExtraTreeClassifier, self).__init__(
          criterion=criterion,
          splitter=splitter,
          max_depth=max_depth,
          min_samples_split=min_samples_split,
          min_samples_leaf=min_samples_leaf,
          min_weight_fraction_leaf=min_weight_fraction_leaf,
          max_features=max_features,
          max_leaf_nodes=max_leaf_nodes,
          class_weight=class_weight,
          min_impurity_split=min_impurity_split,
          random_state=random_state,
      )
  
imports: from __future__ import division
  import numbers
  from abc import ABCMeta
  from abc import abstractmethod
  from math import ceil
  import numpy
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..base import ClassifierMixin
  from ..base import RegressorMixin
  from ..externals import six
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..utils import check_array
  from ..utils import check_random_state
  from ..utils import compute_sample_weight
  from ..utils.multiclass import check_classification_targets
  from ..exceptions import NotFittedError
  from ._criterion import Criterion
  from ._splitter import Splitter
  from ._tree import DepthFirstTreeBuilder
  from ._tree import BestFirstTreeBuilder
  from ._tree import Tree
  from .None import _tree, _splitter, _criterion
label_desc: Use image node_id 1 to create a new ExtraTreeClassifier object from inherited base classes: DecisionTreeClassifier with example: obj = ExtraTreeClassifier(criterion, splitter, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, random_state, max_leaf_nodes, min_impurity_split, class_weight)