mlr_pipeops_scale: Center and Scale Numeric Features

mlr_pipeops_scaleR Documentation

Center and Scale Numeric Features


Centers all numeric features to mean = 0 (if center parameter is TRUE) and scales them by dividing them by their root-mean-square (if scale parameter is TRUE).

The root-mean-square here is defined as sqrt(sum(x^2)/(length(x)-1)). If the center parameter is TRUE, this corresponds to the sd().


R6Class object inheriting from PipeOpTaskPreproc/PipeOp.


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

  • 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 numeric parameters centered and/or scaled.


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

  • center :: numeric
    The mean / median (depending on robust) of each numeric feature during training, or 0 if center is FALSE. Will be subtracted during the predict phase.

  • scale :: numeric
    The value by which features are divided. 1 if scale is FALSE
    If robust is FALSE, this is the root mean square, defined as sqrt(sum(x^2)/(length(x)-1)), of each feature, possibly after centering. If robust is TRUE, this is the mean absolute deviation multiplied by 1.4826 (see stats::mad of each feature, possibly after centering. This is 1 for features that are constant during training if center is TRUE, to avoid division-by-zero.


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

  • center :: logical(1)
    Whether to center features, i.e. subtract their mean() from them. Default TRUE.

  • scale :: logical(1)
    Whether to scale features, i.e. divide them by sqrt(sum(x^2)/(length(x)-1)). Default TRUE.

  • robust :: logical(1)
    Whether to use robust scaling; instead of scaling / centering with mean / standard deviation, median and median absolute deviation mad are used. Initialized to FALSE.


Imitates the scale() function for robust = FALSE and alternatively subtracts the median and divides by mad for robust = TRUE.


Only methods inherited from 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_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_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_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



task = tsk("iris")
pos = po("scale")


one_line_of_iris = task$filter(13)



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