mlr_pipeops_targettrafoscalerange | R Documentation |
Linearly transforms a numeric target of a TaskRegr
so it is between lower
and upper
. The formula for this is x' = offset + x * scale
,
where scale
is (upper - lower) / (max(x) - min(x))
and
offset
is -min(x) * scale + lower
. The same transformation is applied during training and
prediction.
R6Class
object inheriting from PipeOpTargetTrafo
/PipeOp
PipeOpTargetTrafoScaleRange$new(id = "targettrafoscalerange", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "targettrafoscalerange"
.
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 PipeOpTargetTrafo
.
The $state
is a named list
containing the slots $offset
and $scale
.
The parameters are the parameters inherited from PipeOpTargetTrafo
, as well as:
lower
:: numeric(1)
Target value of smallest item of input target. Initialized to 0.
upper
:: numeric(1)
Target value of greatest item of input target. Initialized to 1.
Overloads PipeOpTargetTrafo
's .get_state()
, .transform()
, and
.invert()
. Should be used in combination with PipeOpTargetInvert
.
Only methods inherited from PipeOpTargetTrafo
/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_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_textvectorizer
,
mlr_pipeops_threshold
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
library(mlr3)
task = tsk("boston_housing")
po = PipeOpTargetTrafoScaleRange$new()
po$train(list(task))
po$predict(list(task))
#syntactic sugar for a graph using ppl():
ttscalerange = ppl("targettrafo", trafo_pipeop = PipeOpTargetTrafoScaleRange$new(),
graph = PipeOpLearner$new(LearnerRegrRpart$new()))
ttscalerange$train(task)
ttscalerange$predict(task)
ttscalerange$state$regr.rpart
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