Impute features by their mode. Supports factors as well as logical and numerical features. If multiple modes are present then imputed values are sampled randomly from them.

`R6Class`

object inheriting from `PipeOpImpute`

/`PipeOp`

.

PipeOpImputeMode$new(id = "imputemode", param_vals = list())

`id`

::`character(1)`

Identifier of resulting object, default`"imputemode"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

Input and output channels are inherited from `PipeOpImpute`

.

The output is the input `Task`

with all affected features missing values imputed by (column-wise) mode.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpImpute`

.

The `$state$model`

is a named `list`

of a vector of length one of the type of the feature, indicating the mode of the respective feature.

The parameters are the parameters inherited from `PipeOpImpute`

.

Features that are entirely `NA`

are imputed as
the following: For `factor`

or `ordered`

, random levels are sampled uniformly at random.
For logicals, `TRUE`

or `FALSE`

are sampled uniformly at random.
Numerics and integers are imputed as `0`

.

Note that every random imputation is drawn independently, so different values may be imputed if multiple values are missing.

Only methods inherited from `PipeOpImpute`

/`PipeOp`

.

Other PipeOps:
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreprocSimple`

,
`PipeOpTaskPreproc`

,
`PipeOp`

,
`mlr_pipeops_boxcox`

,
`mlr_pipeops_branch`

,
`mlr_pipeops_chunk`

,
`mlr_pipeops_classbalancing`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_classweights`

,
`mlr_pipeops_colapply`

,
`mlr_pipeops_collapsefactors`

,
`mlr_pipeops_colroles`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_datefeatures`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`mlr_pipeops_encode`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_filter`

,
`mlr_pipeops_fixfactors`

,
`mlr_pipeops_histbin`

,
`mlr_pipeops_ica`

,
`mlr_pipeops_imputeconstant`

,
`mlr_pipeops_imputehist`

,
`mlr_pipeops_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputeoor`

,
`mlr_pipeops_imputesample`

,
`mlr_pipeops_kernelpca`

,
`mlr_pipeops_learner`

,
`mlr_pipeops_missind`

,
`mlr_pipeops_modelmatrix`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_mutate`

,
`mlr_pipeops_nmf`

,
`mlr_pipeops_nop`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_proxy`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_randomprojection`

,
`mlr_pipeops_randomresponse`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_targetinvert`

,
`mlr_pipeops_targetmutate`

,
`mlr_pipeops_targettrafoscalerange`

,
`mlr_pipeops_textvectorizer`

,
`mlr_pipeops_threshold`

,
`mlr_pipeops_tunethreshold`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_updatetarget`

,
`mlr_pipeops_vtreat`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

Other Imputation PipeOps:
`PipeOpImpute`

,
`mlr_pipeops_imputeconstant`

,
`mlr_pipeops_imputehist`

,
`mlr_pipeops_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputeoor`

,
`mlr_pipeops_imputesample`

#> diabetes age glucose insulin mass pedigree pregnant pressure #> 0 0 5 374 11 0 0 35 #> triceps #> 227#> diabetes age pedigree pregnant glucose insulin mass pressure #> 0 0 0 0 0 0 0 0 #> triceps #> 0po$state$model#> $age #> [1] 22 #> #> $glucose #> [1] 100 99 #> #> $insulin #> [1] 105 #> #> $mass #> [1] 32 #> #> $pedigree #> [1] 0.254 0.258 #> #> $pregnant #> [1] 1 #> #> $pressure #> [1] 70 #> #> $triceps #> [1] 32 #>