.. _`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.

: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 decision function.


  - *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 `fit` and `predict`.
    If -1, then the number of jobs is set to the number of cores.


  - *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




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*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 decision function.
    **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 `fit` and `predict`.
        If -1, then the number of jobs is set to the number of cores.
    **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`.

.. automodule:: node_isolationforest

.. class:: IsolationForest