.. _`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*

    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`.




*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

.. class:: IsolationForest