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Isolation Forests in H2O.ai

October 11, 2019 ☼ h2oaiAI Machine Learning

A new feature has been added to H2O-3 open source, isolation forests. I’ve always been a fan of understanding outliers and love using One Class SVMs as a method, but the isolation forests appear to be better in finding outliers, in most cases.

From the H2O.ai blog:

There are multiple approaches to an unsupervised anomaly detection problem that try to exploit the differences between the properties of common and unique observations. The idea behind the Isolation Forest is as follows.

While there’s other methods of outlier detection like LOF (local outlier factor), it appears that Isolation Forests tend to be better than One Class SVMs in finding outliers.

See this handy image from Scikit-Learn site:

Anomaly Detection ComparisonAnomaly Detection Comparison

Interesting indeed. I plan on using this new feature on some work I’m doing for customers.

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