# 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}

• A list

##### code

nbFuncs

##### itemize

• y

##### item

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

##### pkg

caret

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