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

s by connecting
`PipeOpClassifAvg`

to multiple `PipeOpLearner`

outputs.

If the incoming `Learner`

's
`$predict_type`

is set to `"response"`

, the prediction obtained is also a `"response"`

prediction
with each instance predicted to the prediction from incoming `Learner`

s with the
highest total weight. 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.

`R6Class`

inheriting from `PipeOpEnsemble`

/`PipeOp`

.

PipeOpClassifAvg$new(innum = 0, 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.`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 are inherited from `PipeOpEnsemble`

. Instead of a `Prediction`

, a `PredictionClassif`

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`

,
`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_copy`

,
`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_imputehist`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputenewlvl`

,
`mlr_pipeops_imputesample`

,
`mlr_pipeops_kernelpca`

,
`mlr_pipeops_learner`

,
`mlr_pipeops_missind`

,
`mlr_pipeops_modelmatrix`

,
`mlr_pipeops_mutate`

,
`mlr_pipeops_nop`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_yeojohnson`

,
`mlr_pipeops`

Other Ensembles: `PipeOpEnsemble`

,
`mlr_learners_avg`

,
`mlr_pipeops_regravg`

library("mlr3") # Simple Bagging gr = greplicate(n = 5, po("subsample") %>>% po("learner", lrn("classif.rpart")) ) %>>% po("classifavg") mlr3::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