# mlr_pipeops_scale

##### PipeOpScale

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()`

.

- Keywords
- datasets

##### Format

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

##### Construction

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.

##### State

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.

##### Parameters

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`

.

##### Internals

Uses the `scale()`

function.

##### Methods

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

##### See Also

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`

##### Examples

```
# NOT RUN {
library("mlr3")
task = tsk("iris")
pos = po("scale")
pos$train(list(task))[[1]]$data()
one_line_of_iris = task$filter(13)
one_line_of_iris$data()
pos$predict(list(one_line_of_iris))[[1]]$data()
# }
```

*Documentation reproduced from package mlr3pipelines, version 0.1.1, License: LGPL-3*