Encodes columns of type `factor`

, `character`

and `ordered`

.

Impact coding for classification Tasks converts factor levels of each (factorial) column to the difference between each target level's conditional log-likelihood given this level, and the target level's global log-likelihood.

Impact coding for Regression Tasks converts factor levels of each (factorial) column to the difference between the target's conditional mean given this level, and the target's global mean.

Treats new levels during prediction like missing values.

`R6Class`

object inheriting from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpEncodeImpact$new(id = "encodeimpact", param_vals = list())

Identifier of resulting object, default `"encodeimpact"`

.

`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 `factor`

, `character`

or
`ordered`

parameters encoded.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

, as well as:

`impact`

:: a named`list`

A list with an element for each affected feature:

For regression each element is a single column matrix of impact values for each level of that feature.

For classification, it is a list with an element for each*feature level*, which is a vector giving the impact of this feature level on each*outcome level*.

`smoothing`

::`numeric(1)`

A finite positive value used for smoothing. Mostly relevant for classification Tasks if a factor does not coincide with a target factor level (and would otherwise give an infinite logit value). Initialized to`1e-4`

.`impute_zero`

::`logical(1)`

If`TRUE`

, impute missing values as impact 0; otherwise the respective impact is coded as`NA`

. Default`FALSE`

.

Uses laplace smoothing, mostly to avoid infinite values for classification Tasks.

Only methods inherited `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_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`

library("mlr3") poe = po("encodeimpact") task = TaskClassif$new("task", data.table::data.table( x = factor(c("a", "a", "a", "b", "b")), y = factor(c("a", "a", "b", "b", "b"))), "x") poe$train(list(task))[[1]]$data()#> x y.a y.b #> 1: a 9.498089 -9.498089 #> 2: a 9.498089 -9.498089 #> 3: a -1.098546 1.098546 #> 4: b -1.098546 1.098546 #> 5: b -1.098546 1.098546poe$state#> $impact #> $impact$y #> a b #> a 9.498089 -9.498089 #> b -1.098546 1.098546 #> .TEMP.MISSING NA NA #> #> #> $dt_columns #> [1] "y" #> #> $affected_cols #> [1] "y" #> #> $intasklayout #> id type #> 1: y factor #> #> $outtasklayout #> id type #> 1: y.a numeric #> 2: y.b numeric #>