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
# NOT RUN {
library("mlr3")
task = tsk("pima")
task$missings()
po = po("imputemode")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
po$state$model
# }
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