mlr_pipeops_imputemode: Impute Features by their Mode

In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

Impute Features by their Mode

Description

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

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

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: 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

Examples

library("mlr3")

task = tsk("pima")
task$missings()

po = po("imputemode")
new_task = po$train(list(task = task))[[1]]
new_task$missings()

po$state$model

mlr3pipelines documentation built on May 31, 2023, 9:26 p.m.