isotree: Isolation-Based Outlier Detection

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), and fair-cut forest (Cortes (2019) <arXiv:1911.06646>), for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>), and imputation of missing values (Cortes (2019) <arXiv:1911.06646>), based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

Getting started

Package details

AuthorDavid Cortes
MaintainerDavid Cortes <[email protected]>
LicenseBSD_2_clause + file LICENSE
Version0.1.8
URL https://github.com/david-cortes/isotree
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("isotree")

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isotree documentation built on Jan. 8, 2020, 5:07 p.m.