dannymorris/ALSO: Attribute-Wise Learning for Scoring Outliers (ALSO) using Random Forests

ALSO is an anomaly detection algorithm that uses predictive models to produce outlier scores. For a given dataset, separate models are fit for each feature. In each model, one feature is the target and the remaining features are predictors. Observations that consistently deviate from expected (predicted) values across the individual models are scored highly and may represent outliers/anomalies. This package uses random forests (from the ranger package) to build individual regressors and/or classifiers.

Getting started

Package details

Maintainer
LicenseMIT + file LICENSE
Version0.1.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("dannymorris/ALSO")
dannymorris/ALSO documentation built on Aug. 12, 2018, 12:10 p.m.