mlr_pipeops_imputeoor | R Documentation |
Impute factorial features by adding a new level ".MISSING"
.
Impute numerical features by constant values shifted below the minimum or above the maximum by
using min(x) - offset - multiplier * diff(range(x))
or
max(x) + offset + multiplier * diff(range(x))
.
This type of imputation is especially sensible in the context of tree-based methods, see also Ding & Simonoff (2010).
R6Class
object inheriting from PipeOpImpute
/PipeOp
.
PipeOpImputeOOR$new(id = "imputeoor", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "imputeoor"
.
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 are inherited from PipeOpImpute
.
The output is the input Task
with all affected features having missing values imputed as described above.
The $state
is a named list
with the $state
elements inherited from PipeOpImpute
.
The $state$model
contains either ".MISSING"
used for character
and factor
(also
ordered
) features or numeric(1)
indicating the constant value used for imputation of
integer
and numeric
features.
The parameters are the parameters inherited from PipeOpImpute
, as well as:
min
:: logical(1)
Should integer
and numeric
features be shifted below the minimum? Initialized to TRUE. If FALSE
they are shifted above the maximum. See also the description above.
offset
:: numeric(1)
Numerical non-negative offset as used in the description above for integer
and numeric
features. Initialized to 1.
multiplier
:: numeric(1)
Numerical non-negative multiplier as used in the description above for integer
and numeric
features. Initialized to 1.
Adds an explicit new level()
to factor
and ordered
features, but not to character
features.
For integer
and numeric
features uses the min
, max
, diff
and range
functions.
integer
and numeric
features that are entirely NA
are imputed as 0
.
Only methods inherited from PipeOpImpute
/PipeOp
.
Ding Y, Simonoff JS (2010). “An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data.” Journal of Machine Learning Research, 11(6), 131-170. https://jmlr.org/papers/v11/ding10a.html.
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_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
Other Imputation PipeOps:
PipeOpImpute
,
mlr_pipeops_imputeconstant
,
mlr_pipeops_imputehist
,
mlr_pipeops_imputelearner
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputemode
,
mlr_pipeops_imputesample
library("mlr3")
set.seed(2409)
data = tsk("pima")$data()
data$y = factor(c(NA, sample(letters, size = 766, replace = TRUE), NA))
data$z = ordered(c(NA, sample(1:10, size = 767, replace = TRUE)))
task = TaskClassif$new("task", backend = data, target = "diabetes")
task$missings()
po = po("imputeoor")
new_task = po$train(list(task = task))[[1]]
new_task$missings()
new_task$data()
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