.. _`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
:::::::::::::

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.


Definition
::::::::::


Output ports
============

    **model**  model
        Model




Configuration
=============

    **Bootstrap** (bootstrap)
        If True, individual trees are fit on random subsets of the training
        data sampled with replacement. If False, sampling without replacement
        is performed.
    **Contamination** (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'``.
    **Number of features** (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(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.
    **Number of samples** (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)
    **Number of estimators** (n_estimators)
        The number of base estimators in the ensemble.
    **Number of jobs** (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 seed** (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.









Implementation
==============

.. automodule:: node_isolationforest
    :noindex:

.. class:: IsolationForest
    :noindex: