Set the probability threshold the learner should use.

See predict.threshold in makeLearner and setThreshold.

For complex wrappers only the top-level predict.type is currently set.

setPredictThreshold(learner, predict.threshold)

Arguments

learner	(Learner | character(1)) The learner. If you pass a string the learner will be created via makeLearner.
predict.threshold	(numeric) Threshold to produce class labels. Has to be a named vector, where names correspond to class labels. Only for binary classification it can be a single numerical threshold for the positive class. See setThreshold for details on how it is applied. Default is NULL which means 0.5 / an equal threshold for each class.

Value

Learner.

See also

Other predict: asROCRPrediction(), getPredictionProbabilities(), getPredictionResponse(), getPredictionTaskDesc(), predict.WrappedModel(), setPredictType()

Other learner: LearnerProperties, getClassWeightParam(), getHyperPars(), getLearnerId(), getLearnerNote(), getLearnerPackages(), getLearnerParVals(), getLearnerParamSet(), getLearnerPredictType(), getLearnerShortName(), getLearnerType(), getParamSet(), helpLearnerParam(), helpLearner(), makeLearners(), makeLearner(), removeHyperPars(), setHyperPars(), setId(), setLearnerId(), setPredictType()