Impute numerical features by their mean.
R6Class object inheriting from PipeOpImpute/PipeOp.
R6Class
PipeOpImpute
PipeOp
PipeOpImputeMean$new(id = "imputemean", param_vals = list())
id :: character(1) Identifier of resulting object, default "imputemean".
id
character(1)
"imputemean"
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 PipeOpImputeMean.
PipeOpImputeMean
The output is the input Task with all affected numeric features missing values imputed by (column-wise) mean.
Task
The $state is a named list with the $state elements inherited from PipeOpImpute.
$state
The $state$model is a named list of numeric(1) indicating the mean of the respective feature.
$state$model
numeric(1)
The parameters are the parameters inherited from PipeOpImpute.
Uses the mean() function. Features that are entirely NA are imputed as 0.
mean()
NA
0
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_imputemedian, mlr_pipeops_imputenewlvl, mlr_pipeops_imputesample, 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_imputemedian
mlr_pipeops_imputenewlvl
mlr_pipeops_imputesample
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_imputemedian, mlr_pipeops_imputenewlvl, mlr_pipeops_imputesample
# NOT RUN { library("mlr3") task = tsk("pima") task$missings() po = po("imputemean") new_task = po$train(list(task = task))[[1]] new_task$missings() po$state$model # }
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