Provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and outofbag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.
Package details 


Author  Michael Mayer [aut, cre] 
Maintainer  Michael Mayer <mayermichael79@gmail.com> 
License  GPL (>= 2) 
Version  0.1.2 
URL  https://github.com/mayer79/outForest 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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