sbfControl {caret} | R Documentation |
Controls the execution of models with simple filters for feature selection
sbfControl(functions = NULL, method = "boot", saveDetails = FALSE, number = ifelse(method == "cv", 10, 25), verbose = TRUE, returnResamp = "all", p = 0.75, index = NULL, workers = 1, computeFunction = lapply, computeArgs = NULL)
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 |
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. |
workers |
an integer that specifies how many machines/processors will be used |
computeFunction |
a function that is lapply or emulates lapply . It must have arguments X , FUN and ... . computeFunction can be used to build models in parallel. See the examples in sbf . |
computeArgs |
Extra arguments to pass into the ... slore in computeFunction . See the examples in sbf . |
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
y
...
sbf
The function should return a model object that can be used to generate predictions.
The pred
function returns a vector of predictions (numeric or factors) from the current model. The arguments are:
object
fit
functionx
The filter
function is used to return a logical vector with names for each predictor. The values should be TRUE
for predictors that pass the filter and FALSE
for those that do not. Inputs are:
x
y
The function should return a named local vector, as previously stated.
Examples of these functions are included in the package: caretSBF
, lmSBF
, rfSBF
, treebagSBF
, ldaSBF
and nbSBF
.
Model details about these functions, including examples, are in the package vignette for feature selection.
a list that echos the specified arguments
Max Kuhn
sbf
, caretSBF
, lmSBF
, rfSBF
, treebagSBF
, ldaSBF
and nbSBF