Implementation of the Isolation forest method from the paper "Isolation based Anomaly Detection" (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>). An isolation forest measures how easy it is to isolate multivariate observations. Observations that can be isolated with fewer axis-aligned partitions from random decision trees are more likely to be anomalous.
Package details |
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Author | Eric Graves [aut, cre] Ignat Drozdov [ctb] |
Maintainer | Eric Graves <gravcon5@gmail.com> |
License | MIT + file LICENSE |
Version | 1.0.0 |
Package repository | View on GitHub |
Installation |
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