Weighted Prediction AveragingSource:
"response" prediction is a weighted average of the incoming
"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.
Determines the number of input channels. If
innumis 0 (default), a vararg input channel is created that can take an arbitrary number of inputs.
TRUE, the input is a
Multiplicitycollecting channel. This means, a
Multiplicityinput, instead of multiple normal inputs, is accepted and the members are aggregated. This requires
innumto be 0. Default is
character(1)Identifier of the resulting object, default
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
$state is left empty (
The parameters are the parameters inherited from the
PipeOpEnsemble by implementing the
Other Multiplicity PipeOps:
library("mlr3") # Simple Bagging gr = ppl("greplicate", po("subsample") %>>% po("learner", lrn("classif.rpart")), n = 5 ) %>>% po("classifavg") resample(tsk("iris"), GraphLearner$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