caret (version 5.09-012)

sbfControl: Control Object for Selection By Filtering (SBF)

Description

Controls the execution of models with simple filters for feature selection

Usage

sbfControl(functions = NULL, 
           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 = 0.75, 
           index = NULL, 
           timingSamps = 0)

Arguments

functions
a list of functions for model fitting, prediction and variable filtering (see Details below)
method
The external resampling method: boot, cv, LOOCV or LGOCV (for repeated training/test splits
number
Either the number of folds or number of resampling iterations
repeats
For repeated k-fold cross-validation only: the number of complete sets of folds to compute
saveDetails
a logical to save the predictions and variable importances from the selection process
verbose
a logical to print a log for each external resampling iteration
returnResamp
A character string indicating how much of the resampled summary metrics should be saved. Values can be ``all'' or ``none''
p
For leave-group out cross-validation: the training percentage
index
a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.
timingSamps
the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated).

Value

  • a list that echos the specified arguments

code

nbSBF

itemize

  • score

item

  • x
  • y
  • x
  • y

Details

Simple filter-based feature 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 (i.e. after filtering)} y{ the current outcome data (either a numeric or factor vector)} ...{ optional arguments to pass to the fit function in the call to sbf}

See Also

sbf, caretSBF, lmSBF, rfSBF, treebagSBF, ldaSBF and nbSBF