mlr_pipeops_regravg
PipeOpRegrAvg
Perform (weighted) prediction averaging from regression Prediction
s by connecting
PipeOpRegrAvg
to multiple PipeOpLearner
outputs.
The resulting "response"
prediction is a weighted average of the incoming "response"
predictions.
"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.
- Keywords
- datasets
Format
R6Class
inheriting from PipeOpEnsemble
/PipeOp
.
Construction
PipeOpRegrAvg$new(innum = 0, id = "regravg", param_vals = list())
innum
::numeric(1)
Determines the number of input channels. Ifinnum
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"regravg"
.param_vals
:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist()
.
Input and Output Channels
Input and output channels are inherited from PipeOpEnsemble
. Instead of a Prediction
, a PredictionRegr
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
Other PipeOps: PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTaskPreproc
, PipeOp
,
mlr_pipeops_boxcox
,
mlr_pipeops_branch
,
mlr_pipeops_chunk
,
mlr_pipeops_classbalancing
,
mlr_pipeops_classifavg
,
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_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_classifavg
Examples
# NOT RUN {
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"))
# }