Multivariate Outlier Detection and Replacement

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) . 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 out-of-bag 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.


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install.packages("outForest")

0.1.0 by Michael Mayer, 3 months ago


https://github.com/mayer79/outForest


Report a bug at https://github.com/mayer79/outForest/issues


Browse source code at https://github.com/cran/outForest


Authors: Michael Mayer [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports stats, graphics, FNN, ranger, missRanger

Suggests dplyr, knitr


See at CRAN