Fuse learner with a feature filter method.

Description

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.

Usage

makeFilterWrapper(learner, fw.method = "randomForestSRC_importance",
  fw.perc = NULL, fw.abs = NULL, fw.threshold = NULL,
  fw.mandatory.feat = NULL, cache = FALSE, ...)

Arguments

Value

Learner.

Caching

If cache = TRUE, the default mlr cache directory is used to cache filter values. The directory is operating system dependent and can be checked with getCacheDir(). Alternatively a custom directory can be passed to store the cache. The cache can be cleared with deleteCacheDir(). Caching is disabled by default. Care should be taken when operating on large clusters due to possible write conflicts to disk if multiple workers try to write the same cache at the same time.

See Also

Other filter: filterFeatures, generateFilterValuesData, getFilteredFeatures, listFilterMethods, makeFilter, 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

Examples

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)