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 2.19.0 Index]