Splits numeric features into quantile bins.
R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.
R6Class
PipeOpTaskPreprocSimple
PipeOpTaskPreproc
PipeOp
PipeOpQuantileBin$new(id = "quantilebin", param_vals = list())
id :: character(1) Identifier of resulting object, default "quantilebin".
id
character(1)
"quantilebin"
param_vals :: named list List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().
param_vals
list
list()
Input and output channels are inherited from PipeOpTaskPreproc.
The output is the input Task with all affected numeric features replaced by their binned versions.
Task
The $state is a named list with the $state elements inherited from PipeOpTaskPreproc, as well as:
$state
bins :: list List of intervals representing the bins for each numeric feature.
bins
The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:
numsplits :: numeric(1) Number of bins to create. Default is 2.
numsplits
numeric(1)
2
Uses the stats::quantile function.
stats::quantile
Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.
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_regravg, 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
PipeOpEnsemble
PipeOpImpute
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_regravg
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
# NOT RUN { library("mlr3") task = tsk("iris") pop = po("quantilebin") task$data() pop$train(list(task))[[1]]$data() pop$state # }
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