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isotree (version 0.1.28)

Isolation-Based Outlier Detection

Description

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.

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Install

install.packages('isotree')

Monthly Downloads

1,078

Version

0.1.28

License

BSD_2_clause + file LICENSE

Issues

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Maintainer

David Cortes

Last Published

January 13th, 2021

Functions in isotree (0.1.28)

export.isotree.model

Export Isolation Forest model
deepcopy.isotree

Deep-Copy an Isolation Forest Model Object
predict.isolation_forest

Predict method for Isolation Forest
print.isolation_forest

Print summary information from Isolation Forest model
get.num.nodes

Get Number of Nodes per Tree
isotree.to.sql

Generate SQL statements from Isolation Forest model
add.isolation.tree

Add additional (single) tree to isolation forest model
load.isotree.model

Load an Isolation Forest model exported from Python
append.trees

Append isolation trees from one model into another
summary.isolation_forest

Print summary information from Isolation Forest model
unpack.isolation.forest

Unpack isolation forest model after de-serializing
isolation.forest

Create Isolation Forest Model