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 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 of each numeric feature during training, or 0 if`center`

is`FALSE`

. Will be subtracted during the predict phase.`scale`

::`numeric`

The root mean square, defined as`sqrt(sum(x^2)/(length(x)-1))`

, of each feature during training, or 1 if`scale`

is FALSE. During predict phase, features are divided by this.

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`

.

Uses the `scale()`

function.

Only methods inherited from `PipeOpTaskPreproc`

/`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_imputemean`

,
`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_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa -1.3357516 -1.3110521 -0.89767388 1.01560199 #> 2: setosa -1.3357516 -1.3110521 -1.13920048 -0.13153881 #> 3: setosa -1.3923993 -1.3110521 -1.38072709 0.32731751 #> 4: setosa -1.2791040 -1.3110521 -1.50149039 0.09788935 #> 5: setosa -1.3357516 -1.3110521 -1.01843718 1.24503015 #> --- #> 146: virginica 0.8168591 1.4439941 1.03453895 -0.13153881 #> 147: virginica 0.7035638 0.9192234 0.55148575 -1.27867961 #> 148: virginica 0.8168591 1.0504160 0.79301235 -0.13153881 #> 149: virginica 0.9301544 1.4439941 0.43072244 0.78617383 #> 150: virginica 0.7602115 0.7880307 0.06843254 -0.13153881#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa 1.4 0.1 4.8 3#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa -1.335752 -1.442245 -1.259964 -0.1315388