mlr_pipeops_ovrunite | R Documentation |
Perform "One vs. Rest" classification by (weighted) majority vote prediction from classification Predictions. This works in combination with PipeOpOVRSplit
.
Weights can be set as a parameter; if none are provided, defaults to equal weights for each prediction.
Always returns a "prob"
prediction, regardless of the incoming Learner
's
$predict_type
. The label of the class with the highest predicted probability is selected as the
"response"
prediction.
Missing values during prediction are treated as each class label being equally likely.
This PipeOp
uses a Multiplicity
input, which is created by PipeOpOVRSplit
and causes
PipeOp
s on the way to this PipeOp
to be called once for each individual binary Task.
Note that Multiplicity
is currently an experimental features and the implementation or UI
may change.
R6Class
inheriting from PipeOpEnsemble
/PipeOp
.
PipeOpOVRUnite$new(id = "ovrunite", param_vals = list())
id
:: character(1)
Identifier of the resulting object, default "ovrunite"
.
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 and PipeOpEnsemble
's collect
parameter is initialized
with TRUE
to allow for collecting a Multiplicity
input.
The $state
is left empty (list()
).
The parameters are the parameters inherited from the PipeOpEnsemble
.
Inherits from PipeOpEnsemble
by implementing the private$.predict()
method.
Should be used in combination with PipeOpOVRSplit
.
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_classifavg
,
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_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 Ensembles:
PipeOpEnsemble
,
mlr_learners_avg
,
mlr_pipeops_classifavg
,
mlr_pipeops_regravg
Other Multiplicity PipeOps:
Multiplicity()
,
PipeOpEnsemble
,
mlr_pipeops_classifavg
,
mlr_pipeops_featureunion
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_regravg
,
mlr_pipeops_replicate
Other Experimental Features:
Multiplicity()
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_replicate
library(mlr3)
task = tsk("iris")
gr = po("ovrsplit") %>>% lrn("classif.rpart") %>>% po("ovrunite")
gr$train(task)
gr$predict(task)
gr$pipeops$classif.rpart$learner$predict_type = "prob"
gr$predict(task)
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