`R/PipeOpThreshold.R`

`mlr_pipeops_threshold.Rd`

Change the threshold of a `Prediction`

during the `predict`

step.
The incoming `Learner`

's `$predict_type`

needs to be `"prob"`

.
Internally calls `PredictionClassif$set_threshold`

.

`R6Class`

inheriting from `PipeOp`

.

PipeOpThreshold$new(id = "threshold", param_vals = list())

`id`

::`character(1)`

Identifier of the resulting object, default`"threshold"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaults to`numeric(0)`

.

During training, the input and output are `NULL`

.
A `PredictionClassif`

is required as input and returned as output during prediction.

The `$state`

is left empty (`list()`

).

`thresholds`

::`numeric`

A numeric vector of thresholds for the different class levels. May have length 1 for binary classification predictions, must otherwise have length of the number of target classes; see`PredictionClassif`

's`$set_threshold()`

method. Initialized to`0.5`

, i.e. thresholding for binary classification at level`0.5`

.

Only fields inherited from `PipeOp`

.

Only methods inherited from `PipeOp`

.

https://mlr3book.mlr-org.com/list-pipeops.html

Other PipeOps:
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreprocSimple`

,
`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_colroles`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_datefeatures`

,
`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_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_removeconstants`

,
`mlr_pipeops_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_targetinvert`

,
`mlr_pipeops_targetmutate`

,
`mlr_pipeops_targettrafoscalerange`

,
`mlr_pipeops_textvectorizer`

,
`mlr_pipeops_tunethreshold`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_updatetarget`

,
`mlr_pipeops_vtreat`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

library("mlr3") t = tsk("german_credit") gr = po(lrn("classif.rpart", predict_type = "prob")) %>>% po("threshold", param_vals = list(thresholds = 0.9)) gr$train(t) #> $threshold.output #> NULL #> gr$predict(t) #> $threshold.output #> <PredictionClassif> for 1000 observations: #> row_ids truth response prob.good prob.bad #> 1 good bad 0.8767123 0.1232877 #> 2 bad bad 0.1388889 0.8611111 #> 3 good bad 0.8687090 0.1312910 #> --- #> 998 good bad 0.8687090 0.1312910 #> 999 bad bad 0.3795620 0.6204380 #> 1000 good bad 0.7391304 0.2608696 #>