Impute numerical features by histogram.
During training, a histogram is fitted using R's hist() function.
The fitted histogram is then sampled from for imputation. This is an approximation to
sampling from the empirical training data distribution (i.e. sampling from training data
with replacement), but is much more memory efficient for large datasets, since the $state
does not need to save the training data.
R6Class object inheriting from PipeOpImpute/PipeOp.
PipeOpImputeHist$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 lists 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://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_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()
po = po("imputehist")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
po$state$model
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