Backwards Feature Selection Helper Functions
Ancillary fuctions for backwards selection
pickSizeTolerance(x, metric, tol = 1.5, maximize) pickSizeBest(x, metric, maximize)
caretFuncs lmFuncs rfFuncs treebagFuncs ldaFuncs nbFuncs
- a matrix or data frame with the performance metric of interest
- a character string with the name of the performance metric that should be used to choose the appropriate number of variables
- a logical; should the metric be maximized?
- a scalar to denote the acceptable difference in optimal performance (see Details below)
- an integer for the number of variables to retain
This page describes the functions that are used in backwards selection (aka recursive
feature elimination). The funcitons described here are passed to the algorithm via the
functions argument of
rfeControl for details on how these functions should be defined.