Perform "One vs. Rest" classification by (weighted) majority vote prediction from classification Predictions. This works in combination with `PipeOpOVRSplit`

.

Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction.

Always returns a `"prob"`

prediction, regardless of the incoming `Learner`

's
`$predict_type`

. The label of the class with the highest predicted probability is selected as the
`"response"`

prediction.

Missing values during prediction are treated as each class label being equally likely.

This `PipeOp`

uses a `Multiplicity`

input, which is created by `PipeOpOVRSplit`

and causes
`PipeOp`

s on the way to this `PipeOp`

to be called once for each individual binary Task.

Note that `Multiplicity`

is currently an experimental features and the implementation or UI
may change.

`R6Class`

inheriting from `PipeOpEnsemble`

/`PipeOp`

.

PipeOpOVRUnite$new(id = "ovrunite", param_vals = list())

`id`

::`character(1)`

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

.`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 `PipeOpEnsemble`

. Instead of a
`Prediction`

, a `PredictionClassif`

is used as
input and output during prediction and `PipeOpEnsemble`

's `collect`

parameter is initialized
with `TRUE`

to allow for collecting a `Multiplicity`

input.

The `$state`

is left empty (`list()`

).

The parameters are the parameters inherited from the `PipeOpEnsemble`

.

Inherits from `PipeOpEnsemble`

by implementing the `private$.predict()`

method.

Should be used in combination with `PipeOpOVRSplit`

.

Only fields inherited from `PipeOpEnsemble`

/`PipeOp`

.

Only methods inherited from `PipeOpEnsemble`

/`PipeOp`

.

Other PipeOps:
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpProxy`

,
`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_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_nop`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_quantilebin`

,
`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_threshold`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_updatetarget`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

Other Ensembles:
`PipeOpEnsemble`

,
`mlr_learners_avg`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_regravg`

Other Multiplicity PipeOps:
`Multiplicity()`

,
`PipeOpEnsemble`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_replicate`

Other Experimental Features:
`Multiplicity()`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_replicate`

library(mlr3) task = tsk("iris") gr = po("ovrsplit") %>>% lrn("classif.rpart") %>>% po("ovrunite") gr$train(task)#> $ovrunite.output #> NULL #>#> $ovrunite.output #> <PredictionClassif> for 150 observations: #> row_id truth response prob.setosa prob.versicolor prob.virginica #> 1 setosa setosa 1 0 0 #> 2 setosa setosa 1 0 0 #> 3 setosa setosa 1 0 0 #> --- #> 148 virginica virginica 0 0 1 #> 149 virginica virginica 0 0 1 #> 150 virginica virginica 0 0 1 #>#> $ovrunite.output #> <PredictionClassif> for 150 observations: #> row_id truth response prob.setosa prob.versicolor prob.virginica #> 1 setosa setosa 0.9541284 0.00000000 0.04587156 #> 2 setosa setosa 0.9541284 0.00000000 0.04587156 #> 3 setosa setosa 0.9541284 0.00000000 0.04587156 #> --- #> 148 virginica virginica 0.0000000 0.02173913 0.97826087 #> 149 virginica virginica 0.0000000 0.02173913 0.97826087 #> 150 virginica virginica 0.0000000 0.02173913 0.97826087 #>