# mlr_pipeops_histbin

##### PipeOpHistBin

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:

`bins`

::`list`

List of intervals representing the bins for each numeric feature.

##### Parameters

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`bins`

::`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

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

```
# NOT RUN {
library("mlr3")
task = tsk("iris")
pop = po("histbin")
task$data()
pop$train(list(task))[[1]]$data()
pop$state
# }
```

*Documentation reproduced from package mlr3pipelines, version 0.3.0, License: LGPL-3*