Impute features by sampling from non-missing training data.
R6Class object inheriting from PipeOpImpute/PipeOp.
R6Class
PipeOpImpute
PipeOp
PipeOpImputeSample$new(id = "imputesample", param_vals = list())
id :: character(1) Identifier of resulting object, default "imputesample".
id
character(1)
"imputesample"
param_vals :: named list List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().
param_vals
list
list()
Input and output channels are inherited from PipeOpImputeSample.
PipeOpImputeSample
The output is the input Task with all affected numeric features missing values imputed by values sampled (column-wise) from training data.
Task
The $state is a named list with the $state elements inherited from PipeOpImpute.
$state
The $state$model is a named list of training data with missings removed.
$state$model
The parameters are the parameters inherited from PipeOpImpute.
Uses the sample() function. Features that are entirely NA are imputed as the values given by vector() of their type.
sample()
NA
vector()
Only methods inherited from PipeOpImpute/PipeOp.
Other PipeOps: PipeOpEnsemble, PipeOpImpute, 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_copy, 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_imputehist, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputenewlvl, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_mutate, mlr_pipeops_nop, mlr_pipeops_pca, mlr_pipeops_quantilebin, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_scale, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_unbranch, mlr_pipeops_yeojohnson, mlr_pipeops
PipeOpEnsemble
PipeOpTaskPreproc
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_copy
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_imputehist
mlr_pipeops_imputemean
mlr_pipeops_imputemedian
mlr_pipeops_imputenewlvl
mlr_pipeops_kernelpca
mlr_pipeops_learner
mlr_pipeops_missind
mlr_pipeops_modelmatrix
mlr_pipeops_mutate
mlr_pipeops_nop
mlr_pipeops_pca
mlr_pipeops_quantilebin
mlr_pipeops_regravg
mlr_pipeops_removeconstants
mlr_pipeops_scalemaxabs
mlr_pipeops_scalerange
mlr_pipeops_scale
mlr_pipeops_select
mlr_pipeops_smote
mlr_pipeops_spatialsign
mlr_pipeops_subsample
mlr_pipeops_unbranch
mlr_pipeops_yeojohnson
mlr_pipeops
Other Imputation PipeOps: PipeOpImpute, mlr_pipeops_imputehist, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputenewlvl
# 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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