generateFilterValuesData: Calculates feature filter values.

In guillermozbta/mir: Machine Learning in R

Description

Calculates numerical filter values for features. For a list of features, use listFilterMethods.

Usage

generateFilterValuesData(task, method = "randomForestSRC.rfsrc",
  nselect = getTaskNFeats(task), ..., more.args = list())

Arguments

task	[Task] The task.
method	[character] Filter method(s), see above. Default is “randomForestSRC.rfsrc”.
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 if method contains one element.
more.args	[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.

Value

[FilterValues]. A list containing:

task.desc	[TaskDesc] Task description.
data	[data.frame] with columns: name[character] Name of feature. type[character] Feature column type. method[numeric] One column for each method with the feature importance values.

See Also

Other generate_plot_data: generateCalibrationData, generateCritDifferencesData, generateFeatureImportanceData, generateFunctionalANOVAData, generateLearningCurveData, generatePartialDependenceData, generateThreshVsPerfData, getFilterValues, plotFilterValues

Other filter: filterFeatures, getFilterValues, getFilteredFeatures, listFilterMethods, makeFilterWrapper, makeFilter, plotFilterValuesGGVIS, plotFilterValues

guillermozbta/mir documentation built on May 11, 2019, 6:27 p.m.