Package: outForest 1.0.1

outForest: 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) <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 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.

Authors:Michael Mayer [aut, cre]

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Bug tracker:https://github.com/mayer79/outforest/issues

On CRAN:

machine-learningoutlieroutlier-analysisoutlier-detectionrandom-forest

5 exports 13 stars 1.97 score 7 dependencies 1 mentions 15 scripts 229 downloads

Last updated 2 months agofrom:90daee4840. Checks:OK: 7. Indexed: yes.

Exports:DatagenerateOutliersis.outForestoutForestoutliers

Dependencies:FNNlatticeMatrixmissRangerrangerRcppRcppEigen