impute
before training the learner and reimpute
before predicting.
makeImputeWrapper(learner, classes = list(), cols = list(), dummy.classes = character(0L), dummy.cols = character(0L), dummy.type = "factor", impute.new.levels = TRUE, recode.factor.levels = TRUE)
Learner
| character(1)
]
The learner.
If you pass a string the learner will be created via makeLearner
.named list
]
Named list containing imputation techniques for classes of columns.
E.g. list(numeric = imputeMedian())
.named list
]
Named list containing names of imputation methods to impute missing values
in the data column referenced by the list element's name. Overrules imputation set via
classes
.character
]
Classes of columns to create dummy columns for.
Default is character(0)
.character
]
Column names to create dummy columns (containing binary missing indicator) for.
Default is character(0)
.character(1)
]
How dummy columns are encoded. Either as 0/1 with type numeric
or as factor.
Default is factor.logical(1)
]
If new, unencountered factor level occur during reimputation,
should these be handled as NAs and then be imputed the same way?
Default is TRUE
.logical(1)
]
Recode factor levels after reimputation, so they match the respective element of
lvls
(in the description object) and therefore match the levels of the
feature factor in the training data after imputation?.
Default is TRUE
.Learner
].
imputations
,
impute
, makeImputeMethod
,
reimpute
Other wrapper: makeBaggingWrapper
,
makeConstantClassWrapper
,
makeCostSensClassifWrapper
,
makeCostSensRegrWrapper
,
makeDownsampleWrapper
,
makeFeatSelWrapper
,
makeFilterWrapper
,
makeMulticlassWrapper
,
makeMultilabelBinaryRelevanceWrapper
,
makeMultilabelClassifierChainsWrapper
,
makeMultilabelDBRWrapper
,
makeMultilabelNestedStackingWrapper
,
makeMultilabelStackingWrapper
,
makeOverBaggingWrapper
,
makePreprocWrapperCaret
,
makePreprocWrapper
,
makeRemoveConstantFeaturesWrapper
,
makeSMOTEWrapper
,
makeTuneWrapper
,
makeUndersampleWrapper
,
makeWeightedClassesWrapper