.. _example_ensemble_plot_isolation_forest.py:


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IsolationForest example
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An example using IsolationForest for anomaly detection.

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 abnormality and our decision function.

Random partitioning produces noticeable 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.

.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
    Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.




.. image:: images/plot_isolation_forest_001.png
    :align: center




**Python source code:** :download:`plot_isolation_forest.py <plot_isolation_forest.py>`

.. literalinclude:: plot_isolation_forest.py
    :lines: 27-

**Total running time of the example:**  0.49 seconds
( 0 minutes  0.49 seconds)