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.
Format
R6Class object inheriting from PipeOpImpute/PipeOp.
Construction
id::character(1)
Identifier of resulting object, default"imputemode".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
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.
State
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.
Parameters
The parameters are the parameters inherited from PipeOpImpute.
Internals
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.
Methods
Only methods inherited from PipeOpImpute/PipeOp.
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
mlr_pipeops_adas,
mlr_pipeops_blsmote,
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_decode,
mlr_pipeops_encode,
mlr_pipeops_encodeimpact,
mlr_pipeops_encodelmer,
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_learner_pi_cvplus,
mlr_pipeops_learner_quantiles,
mlr_pipeops_missind,
mlr_pipeops_modelmatrix,
mlr_pipeops_multiplicityexply,
mlr_pipeops_multiplicityimply,
mlr_pipeops_mutate,
mlr_pipeops_nearmiss,
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_rowapply,
mlr_pipeops_scale,
mlr_pipeops_scalemaxabs,
mlr_pipeops_scalerange,
mlr_pipeops_select,
mlr_pipeops_smote,
mlr_pipeops_smotenc,
mlr_pipeops_spatialsign,
mlr_pipeops_subsample,
mlr_pipeops_targetinvert,
mlr_pipeops_targetmutate,
mlr_pipeops_targettrafoscalerange,
mlr_pipeops_textvectorizer,
mlr_pipeops_threshold,
mlr_pipeops_tomek,
mlr_pipeops_tunethreshold,
mlr_pipeops_unbranch,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson
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
Examples
library("mlr3")
task = tsk("pima")
task$missings()
#> diabetes age glucose insulin mass pedigree pregnant pressure
#> 0 0 5 374 11 0 0 35
#> triceps
#> 227
po = po("imputemode")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
#> diabetes age pedigree pregnant glucose insulin mass pressure
#> 0 0 0 0 0 0 0 0
#> triceps
#> 0
po$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
#>