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>`.
  
  Parameters
  ----------
  criterion : {"gini", "entropy", "log_loss"}, default="gini"
      The function to measure the quality of a split. Supported criteria are
      "gini" for the Gini impurity and "log_loss" and "entropy" both for the
      Shannon information gain, see :ref:`tree_mathematical_formulation`.
  
  splitter : {"random", "best"}, default="random"
      The strategy used to choose the split at each node. Supported
      strategies are "best" to choose the best split and "random" to choose
      the best random split.
  
  max_depth : int, 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 or float, 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 fraction and
        `ceil(min_samples_split * n_samples)` are the minimum
        number of samples for each split.
  
      .. versionchanged:: 0.18
         Added float values for fractions.
  
  min_samples_leaf : int or float, default=1
      The minimum number of samples required to be at a leaf node.
      A split point at any depth will only be considered if it leaves at
      least ``min_samples_leaf`` training samples in each of the left and
      right branches.  This may have the effect of smoothing the model,
      especially in regression.
  
      - If int, then consider `min_samples_leaf` as the minimum number.
      - If float, then `min_samples_leaf` is a fraction and
        `ceil(min_samples_leaf * n_samples)` are the minimum
        number of samples for each node.
  
      .. versionchanged:: 0.18
         Added float values for fractions.
  
  min_weight_fraction_leaf : float, default=0.0
      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_features : int, float, {"auto", "sqrt", "log2"} or None, default="sqrt"
      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 fraction and
            `max(1, int(max_features * n_features_in_))` features are considered at
            each split.
          - If "sqrt", then `max_features=sqrt(n_features)`.
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          .. versionchanged:: 1.1
              The default of `max_features` changed from `"auto"` to `"sqrt"`.
  
      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.
  
  random_state : int, RandomState instance or None, default=None
      Used to pick randomly the `max_features` used at each split.
      See :term:`Glossary <random_state>` for details.
  
  max_leaf_nodes : int, default=None
      Grow a tree 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_decrease : float, default=0.0
      A node will be split if this split induces a decrease of the impurity
      greater than or equal to this value.
  
      The weighted impurity decrease equation is the following::
  
          N_t / N * (impurity - N_t_R / N_t * right_impurity
                              - N_t_L / N_t * left_impurity)
  
      where ``N`` is the total number of samples, ``N_t`` is the number of
      samples at the current node, ``N_t_L`` is the number of samples in the
      left child, and ``N_t_R`` is the number of samples in the right child.
  
      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
      if ``sample_weight`` is passed.
  
      .. versionadded:: 0.19
  
  class_weight : dict, list of dict or "balanced", default=None
      Weights associated with classes in the form ``{class_label: weight}``.
      If None, 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.
  
      Note that for multioutput (including multilabel) weights should be
      defined for each class of every column in its own dict. For example,
      for four-class multilabel classification weights should be
      [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
      [{1:1}, {2:5}, {3:1}, {4:1}].
  
      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))``
  
      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.
  
  ccp_alpha : non-negative float, default=0.0
      Complexity parameter used for Minimal Cost-Complexity Pruning. The
      subtree with the largest cost complexity that is smaller than
      ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
      :ref:`minimal_cost_complexity_pruning` for details.
  
      .. versionadded:: 0.22
  
  Attributes
  ----------
  classes_ : ndarray of shape (n_classes,) or list of ndarray
      The classes labels (single output problem),
      or a list of arrays of class labels (multi-output problem).
  
  max_features_ : int
      The inferred value of max_features.
  
  n_classes_ : int or list of int
      The number of classes (for single output problems),
      or a list containing the number of classes for each
      output (for multi-output problems).
  
  feature_importances_ : ndarray of shape (n_features,)
      The impurity-based feature importances.
      The higher, the more important the feature.
      The importance of a feature is computed as the (normalized)
      total reduction of the criterion brought by that feature.  It is also
      known as the Gini importance.
  
      Warning: impurity-based feature importances can be misleading for
      high cardinality features (many unique values). See
      :func:`sklearn.inspection.permutation_importance` as an alternative.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  
  tree_ : Tree instance
      The underlying Tree object. Please refer to
      ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
      :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
      for basic usage of these attributes.
  
  See Also
  --------
  ExtraTreeRegressor : An extremely randomized tree regressor.
  sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier.
  sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor.
  sklearn.ensemble.RandomForestClassifier : A random forest classifier.
  sklearn.ensemble.RandomForestRegressor : A random forest regressor.
  sklearn.ensemble.RandomTreesEmbedding : An ensemble of
      totally random trees.
  
  Notes
  -----
  The default values for the parameters controlling the size of the trees
  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
  unpruned trees which can potentially be very large on some data sets. To
  reduce memory consumption, the complexity and size of the trees should be
  controlled by setting those parameter values.
  
  References
  ----------
  
  .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
         Machine Learning, 63(1), 3-42, 2006.
  
  Examples
  --------
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.ensemble import BaggingClassifier
  >>> from sklearn.tree import ExtraTreeClassifier
  >>> X, y = load_iris(return_X_y=True)
  >>> X_train, X_test, y_train, y_test = train_test_split(
  ...    X, y, random_state=0)
  >>> extra_tree = ExtraTreeClassifier(random_state=0)
  >>> cls = BaggingClassifier(extra_tree, random_state=0).fit(
  ...    X_train, y_train)
  >>> cls.score(X_test, y_test)
  0.8947...
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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="sqrt",
      random_state=None,
      max_leaf_nodes=None,
      min_impurity_decrease=0.0,
      class_weight=None,
      ccp_alpha=0.0,
  ):
      super().__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_decrease=min_impurity_decrease,
          random_state=random_state,
          ccp_alpha=ccp_alpha,
      )
  
imports: import copy
  import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from math import ceil
  from numbers import Integral, Real
  import numpy
  from scipy.sparse import issparse
  from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone, is_classifier
  from ..utils import Bunch, check_random_state, compute_sample_weight
  from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _assert_all_finite_element_wise, _check_sample_weight, assert_all_finite, check_is_fitted
  from .None import _criterion, _splitter, _tree
  from ._criterion import Criterion
  from ._splitter import Splitter
  from ._tree import BestFirstTreeBuilder, DepthFirstTreeBuilder, Tree, _build_pruned_tree_ccp, ccp_pruning_path
  from ._utils import _any_isnan_axis0
label_desc: Use image node_id 1 to create a new ExtraTreeClassifier object from inherited base classes: DecisionTreeClassifier with example: obj = ExtraTreeClassifier()