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
id::character(1)
Identifier of resulting object, default"histbin".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
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 acharacter(1)string naming an algorithm to compute the number of cells, anumeric(1)giving the number of breaks for the histogram, a vectornumericgiving the breakpoints between the histogram cells, or afunctionto compute the vector of breakpoints or to compute the number of cells. Default is algorithm"Sturges"(seegrDevices::nclass.Sturges()). For details seehist().
Internals
Uses the graphics::hist function.
Methods
Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
mlr_pipeops_adas,
mlr_pipeops_blsmote,
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_encode,
mlr_pipeops_encodeimpact,
mlr_pipeops_encodelmer,
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_learner_pi_cvplus,
mlr_pipeops_learner_quantiles,
mlr_pipeops_missind,
mlr_pipeops_modelmatrix,
mlr_pipeops_multiplicityexply,
mlr_pipeops_multiplicityimply,
mlr_pipeops_mutate,
mlr_pipeops_nearmiss,
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_rowapply,
mlr_pipeops_scale,
mlr_pipeops_scalemaxabs,
mlr_pipeops_scalerange,
mlr_pipeops_select,
mlr_pipeops_smote,
mlr_pipeops_smotenc,
mlr_pipeops_spatialsign,
mlr_pipeops_subsample,
mlr_pipeops_targetinvert,
mlr_pipeops_targetmutate,
mlr_pipeops_targettrafoscalerange,
mlr_pipeops_textvectorizer,
mlr_pipeops_threshold,
mlr_pipeops_tomek,
mlr_pipeops_tunethreshold,
mlr_pipeops_unbranch,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson
Examples
library("mlr3")
task = tsk("iris")
pop = po("histbin")
task$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <num> <num> <num> <num>
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> ---
#> 146: virginica 5.2 2.3 6.7 3.0
#> 147: virginica 5.0 1.9 6.3 2.5
#> 148: virginica 5.2 2.0 6.5 3.0
#> 149: virginica 5.4 2.3 6.2 3.4
#> 150: virginica 5.1 1.8 5.9 3.0
pop$train(list(task))[[1]]$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <ord> <ord> <ord> <ord>
#> 1: setosa [-Inf,1.5] [-Inf,0.2] (5,5.5] (3.4,3.6]
#> 2: setosa [-Inf,1.5] [-Inf,0.2] (4.5,5] (2.8,3]
#> 3: setosa [-Inf,1.5] [-Inf,0.2] (4.5,5] (3,3.2]
#> 4: setosa [-Inf,1.5] [-Inf,0.2] (4.5,5] (3,3.2]
#> 5: setosa [-Inf,1.5] [-Inf,0.2] (4.5,5] (3.4,3.6]
#> ---
#> 146: virginica (5,5.5] (2.2,2.4] (6.5,7] (2.8,3]
#> 147: virginica (4.5,5] (1.8,2] (6,6.5] (2.4,2.6]
#> 148: virginica (5,5.5] (1.8,2] (6,6.5] (2.8,3]
#> 149: virginica (5,5.5] (2.2,2.4] (6,6.5] (3.2,3.4]
#> 150: virginica (5,5.5] (1.6,1.8] (5.5,6] (2.8,3]
pop$state
#> $breaks
#> $breaks[[1]]
#> [1] -Inf 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 Inf
#>
#> $breaks[[2]]
#> [1] -Inf 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Inf
#>
#> $breaks[[3]]
#> [1] -Inf 4.5 5.0 5.5 6.0 6.5 7.0 7.5 Inf
#>
#> $breaks[[4]]
#> [1] -Inf 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 Inf
#>
#>
#> $dt_columns
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $affected_cols
#> [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width"
#>
#> $intasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: Petal.Length numeric
#> 2: Petal.Width numeric
#> 3: Sepal.Length numeric
#> 4: Sepal.Width numeric
#>
#> $outtasklayout
#> Key: <id>
#> id type
#> <char> <char>
#> 1: Petal.Length ordered
#> 2: Petal.Width ordered
#> 3: Sepal.Length ordered
#> 4: Sepal.Width ordered
#>
#> $outtaskshell
#> Empty data.table (0 rows and 5 cols): Species,Petal.Length,Petal.Width,Sepal.Length,Sepal.Width
#>