Isolation-Based Outlier Detection

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) ), extended isolation forest (Hariri, Kind, Brunner (2018) ), SCiForest (Liu, Ting, Zhou (2010) ), and fair-cut forest (Cortes (2019) ), for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) ), and imputation of missing values (Cortes (2019) ), 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.


Reference manual

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0.2.7 by David Cortes, 3 months ago

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Authors: David Cortes

Documentation:   PDF Manual  

Task views: Missing Data

BSD_2_clause + file LICENSE license

Imports Rcpp

Suggests MASS, outliertree, jsonlite, readr

Enhances Matrix, SparseM

Linking to Rcpp, Rcereal

See at CRAN