Approximate false positive rate control in selection frequency for
random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014)
Significant reduction in compute time for calculating false positive rates by sampling only unique selection frequencies
Addition of tidy
tools (dplyr, tibble, magrittr)
internals
now implemented in C++ via Rcpp
thanks to Dr Jasen Finch (@jasenfinch)Implemented Strategy-1 from Konukoglu,E. and Ganz,M.,2014. Approximate false positive rate control in selection frequency for random forest
Support for randomForest
and ranger
forest objects
Calculate selection frequency threshold for a given false positive rate (alpha)
False positive rate feature selection
Wrapper for selection frequencies extract from objects