caret (version 6.0-34)

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 = "final", 
           p = 0.75, 
           index = NULL, 
           timingSamps = 0,
           seeds = NA,
           allowParallel = TRUE)

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 ``final'' 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).

seeds

an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of NA will stop the seed from being set within the worker processes while a value of

allowParallel

if a parallel backend is loaded and available, should the function use it?

Value

• a list that echos the specified arguments

code

nbSBF

itemize

• score

item

• x
• y
• x
• y

url

http://topepo.github.io/caret/

Details

More details on this function can be found at http://topepo.github.io/caret/featureselection.html#filter.

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

Examples

data(BloodBrain)

## Use a GAM is the filter, then fit a random forest model
RFwithGAM <- sbf(bbbDescr, logBBB,
                 sbfControl = sbfControl(functions = rfSBF,
                                         verbose = FALSE, 
                                         seeds = sample.int(100000, 11),
                                         method = "cv"))
RFwithGAM