.. _`Isolation Forest`:

.. _`org.sysess.sympathy.machinelearning.isolation_forest`:

Isolation Forest
~~~~~~~~~~~~~~~~

.. image:: isolation_forest.svg
   :width: 48


Predicts outliers based on minimum path length of random trees with single nodes in the leafs.



**Documentation**

Predicts outliers based on minimum path length of random trees with single nodes in the leafs.

*Configuration*:


  - *n_estimators*

    The number of base estimators in the ensemble.


  - *max_samples*

    The number of samples to draw from X to train each base estimator  expressed as number of samples (int), or a fraction of all samples (float). If "auto" then a maximum of 256 samples will be used (less when fewer input samples given)

  - *contamination*

    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*

    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_features * X.shape` features.


  - *bootstrap*

    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*

    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 n_jobs for more details.


  - *random_state*

    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 random_state.




*Attributes*:


  - *estimators_samples_*

    The subset of drawn samples (i.e., the in-bag samples) for each base
    estimator.


  - *max_samples_*

    The actual number of samples.




*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_isolationforest
    :noindex:

.. class:: IsolationForest
    :noindex: