mlr_pipeops_imputeoor: Out of Range Imputation

mlr_pipeops_imputeoorR Documentation

Out of Range Imputation


Impute factorial features by adding a new level ".MISSING".

Impute numerical features by constant values shifted below the minimum or above the maximum by using min(x) - offset - multiplier * diff(range(x)) or max(x) + offset + multiplier * diff(range(x)).

This type of imputation is especially sensible in the context of tree-based methods, see also Ding & Simonoff (2010).


R6Class object inheriting from PipeOpImpute/PipeOp.


PipeOpImputeOOR$new(id = "imputeoor", param_vals = list())
  • id :: character(1)
    Identifier of resulting object, default "imputeoor".

  • 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

Input and output channels are inherited from PipeOpImpute.

The output is the input Task with all affected features having missing values imputed as described above.


The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpImpute.

The ⁠$state$model⁠ contains either ".MISSING" used for character and factor (also ordered) features or numeric(1) indicating the constant value used for imputation of integer and numeric features.


The parameters are the parameters inherited from PipeOpImpute, as well as:

  • min :: logical(1)
    Should integer and numeric features be shifted below the minimum? Initialized to TRUE. If FALSE they are shifted above the maximum. See also the description above.

  • offset :: numeric(1)
    Numerical non-negative offset as used in the description above for integer and numeric features. Initialized to 1.

  • multiplier :: numeric(1)
    Numerical non-negative multiplier as used in the description above for integer and numeric features. Initialized to 1.


Adds an explicit new level() to factor and ordered features, but not to character features. For integer and numeric features uses the min, max, diff and range functions. integer and numeric features that are entirely NA are imputed as 0.


Only methods inherited from PipeOpImpute/PipeOp.


Ding Y, Simonoff JS (2010). “An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data.” Journal of Machine Learning Research, 11(6), 131-170.

See Also

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_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_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputesample


data = tsk("pima")$data()
data$y = factor(c(NA, sample(letters, size = 766, replace = TRUE), NA))
data$z = ordered(c(NA, sample(1:10, size = 767, replace = TRUE)))
task = TaskClassif$new("task", backend = data, target = "diabetes")
po = po("imputeoor")
new_task = po$train(list(task = task))[[1]]

mlr3pipelines documentation built on May 31, 2023, 9:26 p.m.