Impute features by sampling from non-missing training data.
R6Class object inheriting from PipeOpImpute/PipeOp.
PipeOpImputeSample$new(id = "imputesample", param_vals = list())
id :: character(1)
Identifier of resulting object, default "imputesample".
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 values sampled (column-wise) from training data.
The $state is a named list with the $state elements inherited from PipeOpImpute.
The $state$model is a named list of training data with missings removed.
The parameters are the parameters inherited from PipeOpImpute.
Uses the sample() function. 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.
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_imputemode,
mlr_pipeops_imputeoor,
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_imputemode,
mlr_pipeops_imputeoor
# NOT RUN {
library("mlr3")
task = tsk("pima")
task$missings()
po = po("imputesample")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
# }
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