mlr_pipeops_encodeimpact: Conditional Target Value Impact Encoding

mlr_pipeops_encodeimpactR Documentation

Conditional Target Value Impact Encoding


Encodes columns of type factor, character and ordered.

Impact coding for classification Tasks converts factor levels of each (factorial) column to the difference between each target level's conditional log-likelihood given this level, and the target level's global log-likelihood.

Impact coding for regression Tasks converts factor levels of each (factorial) column to the difference between the target's conditional mean given this level, and the target's global mean.

Treats new levels during prediction like missing values.


R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.


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

  • 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 PipeOpTaskPreproc.

The output is the input Task with all affected factor, character or ordered parameters encoded.


The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as:

  • impact :: a named list
    A list with an element for each affected feature:
    For regression each element is a single column matrix of impact values for each level of that feature.
    For classification, it is a list with an element for each feature level, which is a vector giving the impact of this feature level on each outcome level.


  • smoothing :: numeric(1)
    A finite positive value used for smoothing. Mostly relevant for classification Tasks if a factor does not coincide with a target factor level (and would otherwise give an infinite logit value). Initialized to 1e-4.

  • impute_zero :: logical(1)
    If TRUE, impute missing values as impact 0; otherwise the respective impact is coded as NA. Default FALSE.


Uses Laplace smoothing, mostly to avoid infinite values for classification Task.


Only methods inherited PipeOpTaskPreproc/PipeOp.

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_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_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


poe = po("encodeimpact")

task = TaskClassif$new("task",
    x = factor(c("a", "a", "a", "b", "b")),
    y = factor(c("a", "a", "b", "b", "b"))),



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