Conducts a Box-Cox transformation on numeric features. The lambda parameter
of the transformation is estimated during training and used for both training
and prediction transformation.
See `bestNormalize::boxcox()`

for details.

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpBoxCox$new(id = "boxcox", param_vals = list())

`id`

::`character(1)`

Identifier of resulting object, default`"boxcox"`

.`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 features replaced by their transformed versions.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

,
as well as a list of class `boxcox`

for each column, which is transformed.

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`standardize`

::`logical(1)`

Whether to center and scale the transformed values to attempt a standard normal distribution. For details see`boxcox()`

.`eps`

::`numeric(1)`

Tolerance parameter to identify if lambda parameter is equal to zero. For details see`boxcox()`

.`lower`

::`numeric(1)`

Lower value for estimation of lambda parameter. For details see`boxcox()`

.`upper`

::`numeric(1)`

Upper value for estimation of lambda parameter. For details see`boxcox()`

.

Uses the `bestNormalize::boxcox`

function.

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

Other PipeOps: `PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTaskPreproc`

, `PipeOp`

,
`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_scale`

,
`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 1.4 0.2 5.1 3.5 #> 2: setosa 1.4 0.2 4.9 3.0 #> 3: setosa 1.3 0.2 4.7 3.2 #> 4: setosa 1.5 0.2 4.6 3.1 #> 5: setosa 1.4 0.2 5.0 3.6 #> --- #> 146: virginica 5.2 2.3 6.7 3.0 #> 147: virginica 5.0 1.9 6.3 2.5 #> 148: virginica 5.2 2.0 6.5 3.0 #> 149: virginica 5.4 2.3 6.2 3.4 #> 150: virginica 5.1 1.8 5.9 3.0#> Error: The following packages could not be loaded: bestNormalizepop$state#> NULL