Perform (weighted) majority vote prediction from classification `Prediction`

s 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.

All incoming `Learner`

's `$predict_type`

must agree.

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.

If `

## Format

`R6Class`

inheriting from `PipeOpEnsemble`

/`PipeOp`

.

## Construction

`PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, id = "classifavg", 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`"classifavg"`

.`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

Input and output channels are inherited from `PipeOpEnsemble`

. Instead of a `Prediction`

, a `PredictionClassif`

is used as input and output during prediction.

## State

The `$state`

is left empty (`list()`

).

## Parameters

The parameters are the parameters inherited from the `PipeOpEnsemble`

.

## Internals

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://mlr-org.com/pipeops.html

Other PipeOps:
`PipeOp`

,
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreproc`

,
`PipeOpTaskPreprocSimple`

,
`mlr_pipeops`

,
`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_encode`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`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_scale`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`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`

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

```
if (requireNamespace("rpart")) {
# \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> with 1 resampling iterations
#> task_id
#> iris
#> learner_id
#> subsample_1.subsample_2.subsample_3.classif.rpart_1.classif.rpart_2.classif.rpart_3.classifavg
#> resampling_id iteration warnings errors
#> holdout 1 0 0
```