Description Usage Arguments Value See Also
Properties can be accessed with getLearnerProperties(learner), which returns a
character vector.
The learner properties are defined as follows:
Can numeric, factor or ordered factor features be handled?
Can missing values in features be handled?
Can observations be weighted during fitting?
Only for classif: Can one-class, two-class or multi-class classification problems be handled?
Only for classif: Can class weights be handled?
Only for surv: Can right, left, or interval censored data be handled?
For classif, cluster, multilabel, surv: Can probabilites be predicted?
Only for regr: Can standard errors be predicted?
Only for classif, regr and surv: Can out of bag predictions be extracted from the trained model?
For classif, regr, surv: Does the model support extracting information on feature importance?
1 2 3 | getLearnerProperties(learner)
hasLearnerProperties(learner, props)
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learner |
[ |
props |
[ |
getLearnerProperties returns a character vector with learner properties.
hasLearnerProperties returns a logical vector of the same length as props.
Other learner: getClassWeightParam,
getHyperPars, getLearnerId,
getLearnerPackages,
getLearnerParVals,
getLearnerParamSet,
getLearnerPredictType,
getLearnerShortName,
getLearnerType, getParamSet,
helpLearnerParam,
helpLearner, makeLearners,
makeLearner, removeHyperPars,
setHyperPars, setId,
setLearnerId,
setPredictThreshold,
setPredictType
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