mlr_pipeops_scale | R Documentation |
Centers all numeric features to mean = 0 (if center
parameter is TRUE
) and scales them
by dividing them by their root-mean-square (if scale
parameter is TRUE
).
The root-mean-square here is defined as sqrt(sum(x^2)/(length(x)-1))
. If the center
parameter
is TRUE
, this corresponds to the sd()
.
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
PipeOpScale$new(id = "scale", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "scale"
.
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 PipeOpTaskPreproc
.
The output is the input Task
with all affected numeric parameters centered and/or scaled.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
, as well as:
center
:: numeric
The mean / median (depending on robust
) of each numeric feature during training, or 0 if center
is FALSE
. Will be subtracted during the predict phase.
scale
:: numeric
The value by which features are divided. 1 if scale
is FALSE
If robust
is FALSE
, this is the root mean square, defined as sqrt(sum(x^2)/(length(x)-1))
, of each feature, possibly after centering.
If robust
is TRUE
, this is the mean absolute deviation multiplied by 1.4826 (see stats::mad of each feature, possibly after centering.
This is 1 for features that are constant during training if center
is TRUE
, to avoid division-by-zero.
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as:
center
:: logical(1)
Whether to center features, i.e. subtract their mean()
from them. Default TRUE
.
scale
:: logical(1)
Whether to scale features, i.e. divide them by sqrt(sum(x^2)/(length(x)-1))
. Default TRUE
.
robust
:: logical(1)
Whether to use robust scaling; instead of scaling / centering with mean / standard deviation,
median and median absolute deviation mad
are used.
Initialized to FALSE
.
Imitates the scale()
function for robust = FALSE
and alternatively subtracts the
median
and divides by mad
for robust = TRUE
.
Only methods inherited from 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_removeconstants
,
mlr_pipeops_renamecolumns
,
mlr_pipeops_replicate
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
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")
task = tsk("iris")
pos = po("scale")
pos$train(list(task))[[1]]$data()
one_line_of_iris = task$filter(13)
one_line_of_iris$data()
pos$predict(list(one_line_of_iris))[[1]]$data()
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