mlr_pipeops_tunethreshold | R Documentation |
Tunes optimal probability thresholds over different PredictionClassif
s.
mlr3::Learner
predict_type
: "prob"
is required.
Thresholds for each learner are optimized using the Optimizer
supplied via
the param_set
.
Defaults to GenSA
.
Returns a single PredictionClassif
.
This PipeOp should be used in conjunction with PipeOpLearnerCV
in order to
optimize thresholds of cross-validated predictions.
In order to optimize thresholds without cross-validation, use PipeOpLearnerCV
in conjunction with ResamplingInsample
.
R6Class
object inheriting from PipeOp
.
* `PipeOpTuneThreshold$new(id = "tunethreshold", param_vals = list())` \cr (`character(1)`, `list`) -> `self` \cr
id
:: character(1)
Identifier of resulting object. Default: "tunethreshold".
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 PipeOp
.
The $state
is a named list
with elements
thresholds
:: numeric
learned thresholds
The parameters are the parameters inherited from PipeOp
, as well as:
measure
:: Measure
| character
Measure
to optimize for.
Will be converted to a Measure
in case it is character
.
Initialized to "classif.ce"
, i.e. misclassification error.
optimizer
:: Optimizer
|character(1)
Optimizer
used to find optimal thresholds.
If character
, converts to Optimizer
via opt
. Initialized to OptimizerGenSA
.
log_level
:: character(1)
| integer(1)
Set a temporary log-level for lgr::get_logger("bbotk")
. Initialized to: "warn".
Uses the optimizer
provided as a param_val
in order to find an optimal threshold.
See the optimizer
parameter for more info.
Only methods inherited from PipeOp
.
https://mlr3book.mlr-org.com/list-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_targettrafoscalerange
,
mlr_pipeops_textvectorizer
,
mlr_pipeops_threshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
library("mlr3") task = tsk("iris") pop = po("learner_cv", lrn("classif.rpart", predict_type = "prob")) %>>% po("tunethreshold") task$data() pop$train(task) pop$state
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