sbfControl
From caret v4.47
by Max Kuhn
Control Object for Selection By Filtering (SBF)
Controls the execution of models with simple filters for feature selection
- Keywords
- utilities
Usage
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)
Arguments
- functions
- a list of functions for model fitting, prediction and variable filtering (see Details below)
- method
- The external resampling method:
boot
,cv
,LOOCV
orLGOCV
(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 emulateslapply
. It must have argumentsX
,FUN
and...
.computeFunction
can be used to build models in parall - computeArgs
- Extra arguments to pass into the
...
slore incomputeFunction
. See the examples insbf
.
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
y
...
sbf
}
Value
- a list that echos the specified arguments
code
itemize
score
item
x
y
x
y
See Also
Community examples
Looks like there are no examples yet.