Perform (weighted) prediction averaging from regression `Prediction`

s by connecting
`PipeOpRegrAvg`

to multiple `PipeOpLearner`

outputs.

The resulting `"response"`

prediction is a weighted average of the incoming `"response"`

predictions.
`"se"`

prediction is currently not aggregated but discarded if present.

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

`R6Class`

inheriting from `PipeOpEnsemble`

/`PipeOp`

.

PipeOpRegrAvg$new(innum = 0, collect_multiplicity = FALSE, id = "regravg", param_vals = list())

`innum`

::`numeric(1)`

Determines the number of input channels. If`innum`

is 0 (default), a vararg input channel is created that can take an arbitrary number of inputs.`collect_multiplicity`

::`logical(1)`

If`TRUE`

, the input is a`Multiplicity`

collecting channel. This means, a`Multiplicity`

input, instead of multiple normal inputs, is accepted and the members are aggregated. This requires`innum`

to be 0. Default is`FALSE`

.`id`

::`character(1)`

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

.`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 `PredictionRegr`

is used as input and output during prediction.

The `$state`

is left empty (`list()`

).

The parameters are the parameters inherited from the `PipeOpEnsemble`

.

Inherits from `PipeOpEnsemble`

by implementing the `private$weighted_avg_predictions()`

method.

Only fields inherited from `PipeOpEnsemble`

/`PipeOp`

.

Only methods inherited from `PipeOpEnsemble`

/`PipeOp`

.

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_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_classifavg`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_replicate`

Other Ensembles:
`PipeOpEnsemble`

,
`mlr_learners_avg`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_ovrunite`

library("mlr3") # Simple Bagging gr = ppl("greplicate", po("subsample") %>>% po("learner", lrn("classif.rpart")), n = 5 ) %>>% po("classifavg") resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))#> <ResampleResult> of 1 iterations #> * Task: iris #> * Learner: subsample_1.subsample_2.subsample_3.subsample_4.subsample_5.classif.rpart_1.classif.rpart_2.classif.rpart_3.classif.rpart_4.classif.rpart_5.classifavg #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations