rfeControl

0th

Percentile

Controlling the Feature Selection Algorithms

This function generates a control object that can be used to specify the details of the feature selection algorithms used in this package.

Keywords
utilities
Usage
rfeControl(functions = NULL,
           rerank = FALSE,
           method = "boot",
           saveDetails = FALSE,
           number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
           repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
           verbose = FALSE,
           returnResamp = "all",
           p = .75,
           index = NULL,
           timingSamps = 0)
Details

Backwards selection requires function to be specified for some operations.

The fit function builds the model based on the current data set. The arguments for the function must be:

  • x
{ the current training set of predictor data with the appropriate subset of variables} y{ the current outcome data (either a numeric or factor vector)} first{ a single logical value for whether the current predictor set has all possible variables} last{ similar to first, but TRUE when the last model is fit with the final subset size and predictors.} ...{optional arguments to pass to the fit function in the call to rfe}

Value

  • A list

code

nbFuncs

itemize

  • y

item

  • x
  • x
  • y
  • metric
  • maximize
  • size

pkg

caret

See Also

rfe, lmFuncs, rfFuncs, treebagFuncs, nbFuncs, pickSizeBest, pickSizeTolerance

Aliases
Documentation reproduced from package caret, version 5.07-001, License: GPL-2

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