mlr_pipeops_removeconstants | R Documentation |
Remove constant features from a mlr3::Task. For each feature, calculates the ratio of features which differ from their mode value. All features with a ratio below a settable threshold are removed from the task. Missing values can be ignored or treated as a regular value distinct from non-missing values.
R6Class
object inheriting from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
PipeOpRemoveConstants$new(id = "removeconstants")
id
:: character(1)
Identifier of the resulting object, defaulting to "removeconstants"
.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list()
.
$state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
, as well as:
features
:: character()
Names of features that are being kept. Features of types that the Filter
can not operate on are always being kept.
The parameters are the parameters inherited from the PipeOpTaskPreproc
, as well as:
ratio
:: numeric(1)
Ratio of values which must be different from the mode value in order to keep a feature in the task.
Initialized to 0, which means only constant features with exactly one observed level are removed.
rel_tol
:: numeric(1)
Relative tolerance within which to consider a numeric feature constant. Set to 0 to disregard relative tolerance. Initialized to 1e-8
.
abs_tol
:: numeric(1)
Absolute tolerance within which to consider a numeric feature constant. Set to 0 to disregard absolute tolerance. Initialized to 1e-8
.
na_ignore
:: logical(1)
If TRUE
, the ratio is calculated after removing all missing values first, so a column can be "constant" even if some but not all values are NA
.
Initialized to TRUE
.
Fields inherited from PipeOpTaskPreproc
/PipeOp
.
Methods inherited from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
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_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_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
library("mlr3")
data = data.table::data.table(y = runif(10), a = 1:10, b = rep(1, 10), c = rep(1:2, each = 5))
task = TaskRegr$new("example", data, target = "y")
po = po("removeconstants")
po$train(list(task = task))[[1]]$data()
po$state
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.