Perform (weighted) majority vote prediction from classification
Predictions by connecting
PipeOpClassifAvg to multiple
Always returns a
"prob" prediction, regardless of the incoming
$predict_type. The label of the class with the highest predicted probability is selected as the
"response" prediction. If the
$predict_type is set to
the prediction obtained is also a
"prob" type prediction with the probability predicted to be a
weighted average of incoming predictions.
$predict_type must agree.
Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction. Defaults to equal weights for each model.
R6Class inheriting from
PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, id = "classifavg", 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.
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.
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
Input and output channels are inherited from
PipeOpEnsemble. Instead of a
is used as input and output during prediction.
$state is left empty (
The parameters are the parameters inherited from the
PipeOpEnsemble by implementing the
Only fields inherited from
Only methods inherited from
Other Multiplicity PipeOps:
library("mlr3") # Simple Bagging gr = ppl("greplicate", po("subsample") %>>% po("learner", lrn("classif.rpart")), n = 3 ) %>>% po("classifavg") resample(tsk("iris"), GraphLearner$new(gr), rsmp("holdout"))
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