class_docstr: Isolation Forest Algorithm.
  
  Return the anomaly score of each sample using the IsolationForest algorithm
  
  The IsolationForest 'isolates' observations by randomly selecting a feature
  and then randomly selecting a split value between the maximum and minimum
  values of the selected feature.
  
  Since recursive partitioning can be represented by a tree structure, the
  number of splittings required to isolate a sample is equivalent to the path
  length from the root node to the terminating node.
  
  This path length, averaged over a forest of such random trees, is a
  measure of normality and our decision function.
  
  Random partitioning produces noticeably shorter paths for anomalies.
  Hence, when a forest of random trees collectively produce shorter path
  lengths for particular samples, they are highly likely to be anomalies.
  
  Read more in the :ref:`User Guide <isolation_forest>`.
  
  .. versionadded:: 0.18
  
  Parameters
  ----------
  n_estimators : int, default=100
      The number of base estimators in the ensemble.
  
  max_samples : "auto", int or float, default="auto"
      The number of samples to draw from X to train each base estimator.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples.
          - If "auto", then `max_samples=min(256, n_samples)`.
  
      If max_samples is larger than the number of samples provided,
      all samples will be used for all trees (no sampling).
  
  contamination : 'auto' or float, default='auto'
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set. Used when fitting to define the threshold
      on the scores of the samples.
  
          - If 'auto', the threshold is determined as in the
            original paper.
          - If float, the contamination should be in the range (0, 0.5].
  
      .. versionchanged:: 0.22
         The default value of ``contamination`` changed from 0.1
         to ``'auto'``.
  
  max_features : int or float, default=1.0
      The number of features to draw from X to train each base estimator.
  
          - If int, then draw `max_features` features.
          - If float, then draw `max(1, int(max_features * n_features_in_))` features.
  
      Note: using a float number less than 1.0 or integer less than number of
      features will enable feature subsampling and leads to a longer runtime.
  
  bootstrap : bool, default=False
      If True, individual trees are fit on random subsets of the training
      data sampled with replacement. If False, sampling without replacement
      is performed.
  
  n_jobs : int, default=None
      The number of jobs to run in parallel for both :meth:`fit` and
      :meth:`predict`. ``None`` means 1 unless in a
      :obj:`joblib.parallel_backend` context. ``-1`` means using all
      processors. See :term:`Glossary <n_jobs>` for more details.
  
  random_state : int, RandomState instance or None, default=None
      Controls the pseudo-randomness of the selection of the feature
      and split values for each branching step and each tree in the forest.
  
      Pass an int for reproducible results across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  verbose : int, default=0
      Controls the verbosity of the tree building process.
  
  warm_start : bool, 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. See :term:`the Glossary <warm_start>`.
  
      .. versionadded:: 0.21
  
  Attributes
  ----------
  estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` instance
      The child estimator template used to create the collection of
      fitted sub-estimators.
  
      .. versionadded:: 1.2
         `base_estimator_` was renamed to `estimator_`.
  
  estimators_ : list of ExtraTreeRegressor instances
      The collection of fitted sub-estimators.
  
  estimators_features_ : list of ndarray
      The subset of drawn features for each base estimator.
  
  estimators_samples_ : list of ndarray
      The subset of drawn samples (i.e., the in-bag samples) for each base
      estimator.
  
  max_samples_ : int
      The actual number of samples.
  
  offset_ : float
      Offset used to define the decision function from the raw scores. We
      have the relation: ``decision_function = score_samples - offset_``.
      ``offset_`` is defined as follows. When the contamination parameter is
      set to "auto", the offset is equal to -0.5 as the scores of inliers are
      close to 0 and the scores of outliers are close to -1. When a
      contamination parameter different than "auto" is provided, the offset
      is defined in such a way we obtain the expected number of outliers
      (samples with decision function < 0) in training.
  
      .. versionadded:: 0.20
  
  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
  
  See Also
  --------
  sklearn.covariance.EllipticEnvelope : An object for detecting outliers in a
      Gaussian distributed dataset.
  sklearn.svm.OneClassSVM : Unsupervised Outlier Detection.
      Estimate the support of a high-dimensional distribution.
      The implementation is based on libsvm.
  sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection
      using Local Outlier Factor (LOF).
  
  Notes
  -----
  The implementation is based on an ensemble of ExtraTreeRegressor. The
  maximum depth of each tree is set to ``ceil(log_2(n))`` where
  :math:`n` is the number of samples used to build the tree
  (see (Liu et al., 2008) for more details).
  
  References
  ----------
  .. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
         Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
  .. [2] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based
         anomaly detection." ACM Transactions on Knowledge Discovery from
         Data (TKDD) 6.1 (2012): 3.
  
  Examples
  --------
  >>> from sklearn.ensemble import IsolationForest
  >>> X = [[-1.1], [0.3], [0.5], [100]]
  >>> clf = IsolationForest(random_state=0).fit(X)
  >>> clf.predict([[0.1], [0], [90]])
  array([ 1,  1, -1])
  
  For an example of using isolation forest for anomaly detection see
  :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py`.
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code: def __init__(
      self,
      *,
      n_estimators=100,
      max_samples="auto",
      contamination="auto",
      max_features=1.0,
      bootstrap=False,
      n_jobs=None,
      random_state=None,
      verbose=0,
      warm_start=False,
  ):
      super().__init__(
          estimator=ExtraTreeRegressor(
              max_features=1,
              splitter="random",
              random_state=random_state,
          ),
          # here above max_features has no links with self.max_features
          bootstrap=bootstrap,
          bootstrap_features=False,
          n_estimators=n_estimators,
          max_samples=max_samples,
          max_features=max_features,
          warm_start=warm_start,
          n_jobs=n_jobs,
          random_state=random_state,
          verbose=verbose,
      )
  
      self.contamination = contamination
  
imports: import numbers
  from numbers import Integral, Real
  from warnings import warn
  import numpy
  from scipy.sparse import issparse
  from ..base import OutlierMixin, _fit_context
  from ..tree import ExtraTreeRegressor
  from ..tree._tree import DTYPE
  from ..utils import check_array, check_random_state, gen_batches, get_chunk_n_rows
  from ..utils._param_validation import Interval, RealNotInt, StrOptions
  from ..utils.validation import _num_samples, check_is_fitted
  from ._bagging import BaseBagging
label_desc: Use image node_id 1 to create a new IsolationForest object from inherited base classes: OutlierMixin, BaseBagging with example: obj = IsolationForest()