Calculates numerical filter values for features. For a list of features, use listFilterMethods.
generateFilterValuesData(
task,
method = "randomForestSRC_importance",
nselect = getTaskNFeats(task),
...,
more.args = list()
)(Task) The task.
(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 if method contains one
element.
(named list)
Extra args passed down to filter methods. List elements are named with the
filter method name the args should be passed down to.
A more general and flexible option than ....
Default is empty list.
(FilterValues). A list containing:
[TaskDesc) Task description.
(data.frame) with columns:
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.
Other generate_plot_data:
generateCalibrationData(),
generateCritDifferencesData(),
generateFeatureImportanceData(),
generateLearningCurveData(),
generatePartialDependenceData(),
generateThreshVsPerfData(),
plotFilterValues()
Other filter:
filterFeatures(),
getFilteredFeatures(),
listFilterEnsembleMethods(),
listFilterMethods(),
makeFilterEnsemble(),
makeFilterWrapper(),
makeFilter(),
plotFilterValues()
# NOT RUN {
# 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")))
# }
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