Perform (weighted) majority vote prediction from classification Predictions by connecting PipeOpClassifAvg to multiple PipeOpLearner outputs.

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. If the Learner's $predict_type is set to "prob", the prediction obtained is also a "prob" type prediction with the probability predicted to be a weighted average of incoming predictions.

## Parameters

The parameters are the parameters inherited from the PipeOpEnsemble.

Inherits from PipeOpEnsemble by implementing the private$weighted_avg_predictions() method. ## Fields Only fields inherited from PipeOpEnsemble/PipeOp. ## Methods Only methods inherited from PipeOpEnsemble/PipeOp. ## See also 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_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_threshold, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson, mlr_pipeops Other Multiplicity PipeOps: Multiplicity(), PipeOpEnsemble, mlr_pipeops_featureunion, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_regravg, mlr_pipeops_replicate Other Ensembles: PipeOpEnsemble, mlr_learners_avg, mlr_pipeops_ovrunite, mlr_pipeops_regravg ## Examples # \donttest{ library("mlr3") # Simple Bagging gr = ppl("greplicate", po("subsample") %>>% po("learner", lrn("classif.rpart")), n = 3 ) %>>% po("classifavg") resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))
#> <ResampleResult> of 1 iterations
`