mlr_pipeops_histbin: Split Numeric Features into Equally Spaced Bins

In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

Split Numeric Features into Equally Spaced Bins

Description

Splits numeric features into equally spaced bins. See graphics::hist() for details. Values that fall out of the training data range during prediction are binned with the lowest / highest bin respectively.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpHistBin$new(id = "histbin", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "histbin".

• 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

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with all affected numeric features replaced by their binned versions.

State

The ⁠$state⁠ is a named list with the ⁠$state⁠ elements inherited from PipeOpTaskPreproc, as well as:

• breaks :: list
  List of intervals representing the bins for each numeric feature.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• breaks :: character(1) | numeric | function
  Either a character(1) string naming an algorithm to compute the number of cells, a numeric(1) giving the number of breaks for the histogram, a vector numeric giving the breakpoints between the histogram cells, or a function to compute the vector of breakpoints or to compute the number of cells. Default is algorithm "Sturges" (see grDevices::nclass.Sturges()). For details see hist().

Internals

Uses the graphics::hist function.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See Also

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_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

Examples

library("mlr3")

task = tsk("iris")
pop = po("histbin")

task$data()
pop$train(list(task))[[1]]$data()

pop$state

mlr3pipelines documentation built on May 31, 2023, 9:26 p.m.