makeFilterWrapper: Fuse learner with a feature filter method.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

Fuses a base learner with a filter method. Creates a learner object, which can be used like any other learner object. Internally uses filterFeatures before every model fit.

After training, the selected features can be retrieved with getFilteredFeatures.

Note that observation weights do not influence the filtering and are simply passed down to the next learner.

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makeFilterWrapper(learner, fw.method = "randomForestSRC.rfsrc",
  fw.perc = NULL, fw.abs = NULL, fw.threshold = NULL,
  fw.mandatory.feat = NULL, ...)

`learner`	[`Learner` \| `character(1)`] The learner. If you pass a string the learner will be created via `makeLearner`.
`fw.method`	[`character(1)`] Filter method. See `listFilterMethods`. Default is “randomForestSRC.rfsrc”.
`fw.perc`	[`numeric(1)`] If set, select `fw.perc`*100 top scoring features. Mutually exclusive with arguments `fw.abs` and `fw.threshold`.
`fw.abs`	[`numeric(1)`] If set, select `fw.abs` top scoring features. Mutually exclusive with arguments `fw.perc` and `fw.threshold`.
`fw.threshold`	[`numeric(1)`] If set, select features whose score exceeds `fw.threshold`. Mutually exclusive with arguments `fw.perc` and `fw.abs`.
`fw.mandatory.feat`	[`character`] Mandatory features which are always included regardless of their scores
`...`	[any] Additional parameters passed down to the filter.

[Learner].

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

Other wrapper: makeBaggingWrapper, makeClassificationViaRegressionWrapper, makeConstantClassWrapper, makeCostSensClassifWrapper, makeCostSensRegrWrapper, makeDownsampleWrapper, makeDummyFeaturesWrapper, makeExtractFDAFeatsWrapper, makeFeatSelWrapper, makeImputeWrapper, makeMulticlassWrapper, makeMultilabelBinaryRelevanceWrapper, makeMultilabelClassifierChainsWrapper, makeMultilabelDBRWrapper, makeMultilabelNestedStackingWrapper, makeMultilabelStackingWrapper, makeOverBaggingWrapper, makePreprocWrapperCaret, makePreprocWrapper, makeRemoveConstantFeaturesWrapper, makeSMOTEWrapper, makeTuneWrapper, makeUndersampleWrapper, makeWeightedClassesWrapper

task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda")
inner = makeResampleDesc("Holdout")
outer = makeResampleDesc("CV", iters = 2)
lrn = makeFilterWrapper(lrn, fw.perc = 0.5)
mod = train(lrn, task)
print(getFilteredFeatures(mod))
# now nested resampling, where we extract the features that the filter method selected
r = resample(lrn, task, outer, extract = function(model) {
  getFilteredFeatures(model)
})
print(r$extract)