mlr_pipeops_classifavg | R Documentation |
Perform (weighted) majority vote prediction from classification Prediction
s by connecting
PipeOpClassifAvg
to multiple PipeOpLearner
outputs.
Always returns a "prob"
prediction, regardless of the incoming Learner
's
$predict_type
. The label of the class with the highest predicted probability is selected as the
"response"
prediction. 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 $predict_type
must agree.
Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction. Defaults to equal weights for each model.
If '
R6Class
inheriting from PipeOpEnsemble
/PipeOp
.
PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, 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.
collect_multiplicity
:: logical(1)
If 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.
Default is FALSE
.
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
.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreprocSimple
,
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_colroles
,
mlr_pipeops_copy
,
mlr_pipeops_datefeatures
,
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_imputeconstant
,
mlr_pipeops_imputehist
,
mlr_pipeops_imputelearner
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputemode
,
mlr_pipeops_imputeoor
,
mlr_pipeops_imputesample
,
mlr_pipeops_kernelpca
,
mlr_pipeops_learner
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_mutate
,
mlr_pipeops_nmf
,
mlr_pipeops_nop
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_ovrunite
,
mlr_pipeops_pca
,
mlr_pipeops_proxy
,
mlr_pipeops_quantilebin
,
mlr_pipeops_randomprojection
,
mlr_pipeops_randomresponse
,
mlr_pipeops_regravg
,
mlr_pipeops_removeconstants
,
mlr_pipeops_renamecolumns
,
mlr_pipeops_replicate
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_scale
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_spatialsign
,
mlr_pipeops_subsample
,
mlr_pipeops_targetinvert
,
mlr_pipeops_targetmutate
,
mlr_pipeops_targettrafoscalerange
,
mlr_pipeops_textvectorizer
,
mlr_pipeops_threshold
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
Other Multiplicity PipeOps:
Multiplicity()
,
PipeOpEnsemble
,
mlr_pipeops_featureunion
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_ovrunite
,
mlr_pipeops_regravg
,
mlr_pipeops_replicate
Other Ensembles:
PipeOpEnsemble
,
mlr_learners_avg
,
mlr_pipeops_ovrunite
,
mlr_pipeops_regravg
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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