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Backwards Feature Selection Helper Functions

Ancillary fuctions for backwards selection

Keywords
models
Usage
pickSizeTolerance(x, metric, tol = 1.5, maximize) 
pickSizeBest(x, metric, maximize) 

pickVars(y, size)

caretFuncs lmFuncs rfFuncs treebagFuncs ldaFuncs nbFuncs

Arguments
x
a matrix or data frame with the performance metric of interest
metric
a character string with the name of the performance metric that should be used to choose the appropriate number of variables
maximize
a logical; should the metric be maximized?
tol
a scalar to denote the acceptable difference in optimal performance (see Details below)
y
a list of data frames with variables Overall and var
size
an integer for the number of variables to retain
Details

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.

See rfeControl for details on how these functions should be defined.

See Also

rfeControl, rfe

Aliases
  • caretFuncs
  • lmFuncs
  • rfFuncs
  • treebagFuncs
  • ldaFuncs
  • nbFuncs
  • pickSizeBest
  • pickSizeTolerance
  • pickVars
Documentation reproduced from package caret, version 4.25, License: GPL-2

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