Calculates numerical filter values for features. For a list of features, use listFilterMethods.
Usage
generateFilterValuesData(
task,
method = "FSelectorRcpp_information.gain",
nselect = getTaskNFeats(task),
...,
more.args = list()
)
Arguments
- task
(Task)
The task.- method
(character | list)
Filter method(s). In case of ensemble filters thelist
notation needs to be used. See the examples for more information. Default is “FSelectorRcpp_information.gain”.- nselect
(
integer(1)
)
Number of scores to request. Scores are getting calculated for all features per default.- ...
(any)
Passed down to selected method. Can only be use ifmethod
contains one element.- more.args
(named list)
Extra args passed down to filter methods. List elements are named with the filtermethod
name the args should be passed down to. A more general and flexible option than...
. Default is empty list.
Simple and ensemble filters
Besides passing (multiple) simple filter methods you can also pass an
ensemble filter method (in a list). The ensemble method will use the simple
methods to calculate its ranking. See listFilterEnsembleMethods()
for
available ensemble methods.
See also
Other generate_plot_data:
generateCalibrationData()
,
generateCritDifferencesData()
,
generateFeatureImportanceData()
,
generateLearningCurveData()
,
generatePartialDependenceData()
,
generateThreshVsPerfData()
,
plotFilterValues()
Other filter:
filterFeatures()
,
getFilteredFeatures()
,
listFilterEnsembleMethods()
,
listFilterMethods()
,
makeFilterEnsemble()
,
makeFilterWrapper()
,
makeFilter()
,
plotFilterValues()
Examples
# two simple filter methods
fval = generateFilterValuesData(iris.task,
method = c("FSelectorRcpp_gain.ratio", "FSelectorRcpp_information.gain"))
# using ensemble method "E-mean"
fval = generateFilterValuesData(iris.task,
method = list("E-mean", c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain")))