Remove constant features from a mlr3::Task. For each feature, calculates the ratio of features which differ from their mode value. All features with a ratio below a settable threshold are removed from the task. Missing values can be ignored or treated as a regular value distinct from non-missing values.

## Format

`R6Class`

object inheriting from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

## Construction

`id`

::`character(1)`

Identifier of the resulting object, defaulting to`"removeconstants"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

## State

`$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

, as well as:

`features`

::`character()`

Names of features that are being kept. Features of types that the`Filter`

can not operate on are always being kept.

## Parameters

The parameters are the parameters inherited from the `PipeOpTaskPreproc`

, as well as:

`ratio`

::`numeric(1)`

Ratio of values which must be different from the mode value in order to keep a feature in the task. Initialized to 0, which means only constant features with exactly one observed level are removed.`rel_tol`

::`numeric(1)`

Relative tolerance within which to consider a numeric feature constant. Set to 0 to disregard relative tolerance. Initialized to`1e-8`

.`abs_tol`

::`numeric(1)`

Absolute tolerance within which to consider a numeric feature constant. Set to 0 to disregard absolute tolerance. Initialized to`1e-8`

.`na_ignore`

::`logical(1)`

If`TRUE`

, the ratio is calculated after removing all missing values first, so a column can be "constant" even if some but not all values are`NA`

. Initialized to`TRUE`

.

## Fields

Fields inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

## Methods

Methods inherited from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

## See also

https://mlr-org.com/pipeops.html

Other PipeOps:
`PipeOp`

,
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreproc`

,
`PipeOpTaskPreprocSimple`

,
`mlr_pipeops`

,
`mlr_pipeops_adas`

,
`mlr_pipeops_blsmote`

,
`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_encode`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`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_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_rowapply`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_smotenc`

,
`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`

## Examples

```
library("mlr3")
data = data.table::data.table(y = runif(10), a = 1:10, b = rep(1, 10), c = rep(1:2, each = 5))
task = TaskRegr$new("example", data, target = "y")
po = po("removeconstants")
po$train(list(task = task))[[1]]$data()
#> y a c
#> <num> <int> <int>
#> 1: 0.60926702 1 1
#> 2: 0.67714962 2 1
#> 3: 0.77467681 3 1
#> 4: 0.74747736 4 1
#> 5: 0.34581646 5 1
#> 6: 0.82952940 6 2
#> 7: 0.08415901 7 2
#> 8: 0.07145834 8 2
#> 9: 0.06158317 9 2
#> 10: 0.99218066 10 2
po$state
#> $features
#> [1] "a" "c"
#>
#> $affected_cols
#> [1] "a" "b" "c"
#>
#> $intasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: a integer
#> 2: b numeric
#> 3: c integer
#>
#> $outtasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: a integer
#> 2: c integer
#>
#> $outtaskshell
#> Empty data.table (0 rows and 3 cols): y,a,c
#>
```