LearnerProperties | R Documentation |
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 an arbitrary number of functional features be handled?
Can exactly one functional feature 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?
getLearnerProperties(learner)
hasLearnerProperties(learner, props)
learner |
(Learner | |
props |
(character) |
getLearnerProperties
returns a character vector with learner properties.
hasLearnerProperties
returns a logical vector of the same length as props
.
Other learner:
getClassWeightParam()
,
getHyperPars()
,
getLearnerId()
,
getLearnerNote()
,
getLearnerPackages()
,
getLearnerParVals()
,
getLearnerParamSet()
,
getLearnerPredictType()
,
getLearnerShortName()
,
getLearnerType()
,
getParamSet()
,
helpLearnerParam()
,
helpLearner()
,
makeLearners()
,
makeLearner()
,
removeHyperPars()
,
setHyperPars()
,
setId()
,
setLearnerId()
,
setPredictThreshold()
,
setPredictType()
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