"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.
PipeOpRegrAvg$new(innum = 0, id = "regravg", param_vals = list())
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.
Identifier of the resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
$state is left empty (
The parameters are the parameters inherited from the
PipeOpEnsemble by implementing the
library("mlr3") # Simple Bagging gr = greplicate(n = 5, po("subsample") %>>% po("learner", lrn("classif.rpart")) ) %>>% po("classifavg") resample(tsk("iris"), GraphLearner$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