- H2O Release 3.22 (Xia), named after mathematician Zhihong Xia, added Isolation Forest, an unsupervised anomaly detection algorithm based on Distributed Random Forest.
- H2O Release 3.22 integrated XGBoost into the AutoML framework after performance and stability improvements, so XGBoost models now also feed into AutoML's final Stacked Ensembles.
- H2O Release 3.22 extended its Tree API to fetch individual trees from GBM, Distributed Random Forest, XGBoost, and Isolation Forest models in Python and R.
- H2O Release 3.22 made Target Encoding, which encodes categorical variables using the mean of a target variable, available in Java and Python for the first time, having previously been R-only.
- H2O Release 3.22 added a -features startup parameter that lets users restrict H2O to beta, stable, or experimental algorithms when launching the server.
Xia Release (H2O 3.22)
There’s a new major release of H2O and it’s packed with new features and fixes! Among the big new features in this release, we introduce Isolation Forest to our portfolio of machine learning algorithms and integrates the XGBoost algorithm into our AutoML framework. The release is named after Zhihong Xia .
Isolation Forest
Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection. Anomaly detection is applicable to a variety of uses cases, including Fraud Detection or Intrusion Detection. The Isolation Forest algorithm is different from other methods typically used for anomaly detection: it directly identifies the exceptional observations instead of learning the pattern of the normal observations (as is done in the H2O deep learning based autoencoder). The H2O implementation of Isolation Forest is based on the Distributed Random Forest algorithm, so it is capable of analyzing large datasets in multi-node clusters. Note that Isolation Forest is currently in a Beta state. Additional enhancements and improvements will be made in future releases. A blog post is available for more information.