R: Classification via regression wrapper.

makeClassificationViaRegressionWrapper {mlr} | R Documentation

## Classification via regression wrapper.

### Description

Builds regression models that predict for the positive class whether a particular example belongs to it (1) or not (-1).

Probabilities are generated by transforming the predictions with a softmax.

Inspired by WEKA's ClassificationViaRegression (http://weka.sourceforge.net/doc.dev/weka/classifiers/meta/ClassificationViaRegression.html).

### Usage

    makeClassificationViaRegressionWrapper(learner, predict.type = "response")

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`predict.type` | (`character(1)`) “response” (= labels) or “prob” (= probabilities and labels by selecting the one with maximal probability).

### Value

Learner.

### See Also

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

### Examples

    lrn = makeLearner("regr.rpart")
    lrn = makeClassificationViaRegressionWrapper(lrn)
    mod = train(lrn, sonar.task, subset = 1:140)
    predictions = predict(mod, newdata = getTaskData(sonar.task)[141:208, 1:60])

[Package mlr version 2.12.1 Index]