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.acm.org/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 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.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, graphics, FNN, ranger, missRanger (≥ 2.1.0) |
| Suggests: | dplyr, knitr |
| Published: | 2020-01-13 |
| Author: | Michael Mayer [aut, cre] |
| Maintainer: | Michael Mayer <mayermichael79 at gmail.com> |
| BugReports: | https://github.com/mayer79/outForest/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/mayer79/outForest |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | outForest results |
| Reference manual: | outForest.pdf |
| Vignettes: |
outForest |
| Package source: | outForest_0.1.0.tar.gz |
| Windows binaries: | r-devel: outForest_0.1.0.zip, r-release: outForest_0.1.0.zip, r-oldrel: outForest_0.1.0.zip |
| macOS binaries: | r-release: outForest_0.1.0.tgz, r-oldrel: outForest_0.1.0.tgz |
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