Impute numerical features by histogram.

`R6Class`

object inheriting from `PipeOpImpute`

/`PipeOp`

.

PipeOpImputeHist$new(id = "imputehist", param_vals = list())

`id`

::`character(1)`

Identifier of resulting object, default`"imputehist"`

.`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 numeric features missing values imputed by (column-wise) histogram.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpImpute`

.

The `$state$model`

is a named `list`

of `list`

s containing elements `$counts`

and `$breaks`

.

The parameters are the parameters inherited from `PipeOpImpute`

.

Uses the `graphics::hist()`

function. Features that are entirely `NA`

are imputed as `0`

.

Only methods inherited from `PipeOpImpute`

/`PipeOp`

.

https://mlr3book.mlr-org.com/list-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_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputemode`

,
`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_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputemode`

,
`mlr_pipeops_imputeoor`

,
`mlr_pipeops_imputesample`

library("mlr3") task = tsk("pima") task$missings() #> diabetes age glucose insulin mass pedigree pregnant pressure #> 0 0 5 374 11 0 0 35 #> triceps #> 227 po = po("imputehist") new_task = po$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 #> $age$counts #> [1] 267 150 81 76 76 37 31 23 14 11 1 0 1 #> #> $age$breaks #> [1] 20 25 30 35 40 45 50 55 60 65 70 75 80 85 #> #> #> $glucose #> $glucose$counts #> [1] 4 38 167 205 157 91 60 41 #> #> $glucose$breaks #> [1] 40 60 80 100 120 140 160 180 200 #> #> #> $insulin #> $insulin$counts #> [1] 151 158 48 17 11 6 1 1 1 #> #> $insulin$breaks #> [1] 0 100 200 300 400 500 600 700 800 900 #> #> #> $mass #> $mass$counts #> [1] 14 98 180 221 148 61 27 5 2 0 1 #> #> $mass$breaks #> [1] 15 20 25 30 35 40 45 50 55 60 65 70 #> #> #> $pedigree #> $pedigree$counts #> [1] 128 282 154 99 54 22 16 4 4 1 1 2 1 #> #> $pedigree$breaks #> [1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 #> #> #> $pregnant #> $pregnant$counts #> [1] 349 143 107 83 52 20 12 1 1 #> #> $pregnant$breaks #> [1] 0 2 4 6 8 10 12 14 16 18 #> #> #> $pressure #> $pressure$counts #> [1] 3 2 24 94 217 228 127 25 11 1 1 #> #> $pressure$breaks #> [1] 20 30 40 50 60 70 80 90 100 110 120 130 #> #> #> $triceps #> $triceps$counts #> [1] 9 115 179 164 65 7 1 0 0 1 #> #> $triceps$breaks #> [1] 0 10 20 30 40 50 60 70 80 90 100 #> #>