Approximate False Positive Rate Control in Selection Frequency for Random Forest

Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) . Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.


News

NEWS

V0.2.0

  • Significant reduction in compute time for calculating false positive rates by sampling only unique selection frequencies

  • Addition of tidy tools (dplyr, tibble, magrittr)

V0.1.1

  • internals now implemented in C++ via Rcpp thanks to Dr Jasen Finch (@jasenfinch)

v0.1.0

  • 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

Reference manual

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install.packages("forestControl")

0.2.0 by Tom Wilson, 6 months ago


https://github.com/aberHRML/forestControl


Report a bug at https://github.com/aberHRML/forestControl/issues


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


Authors: Tom Wilson [aut, cre] , Jasen Finch [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports Rcpp, purrr, tibble, magrittr, dplyr

Suggests testthat, randomForest, ranger

Linking to Rcpp


See at CRAN