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)
|
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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