mlr_pipeops_blsmote | R Documentation |
Adds new data points by generating synthetic instances for the minority class using the Borderline-SMOTE algorithm.
This can only be applied to classification tasks with numeric features that have no missing values.
See smotefamily::BLSMOTE
for details.
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
PipeOpBLSmote$new(id = "blsmote", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "smote"
.
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 during training is the input Task
with added synthetic rows for the minority class.
The output during prediction is the unchanged input.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreproc
.
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as:
K
:: numeric(1)
The number of nearest neighbors used for sampling from the minority class. Default is 5
.
See BLSMOTE()
.
C
:: numeric(1)
The number of nearest neighbors used for classifying sample points as SAFE/DANGER/NOISE. Default is 5
.
See BLSMOTE()
.
dup_size
:: numeric
Desired times of synthetic minority instances over the original number of majority instances. 0
leads to balancing minority and majority class.
Default is 0
. See BLSMOTE()
.
method
:: character(1)
The type of Borderline-SMOTE algorithm to use. Default is "type1"
.
See BLSMOTE()
.
quiet
:: logical(1)
Whether to suppress printing status during training. Initialized to TRUE
.
Only fields inherited from PipeOpTaskPreproc
/PipeOp
.
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
Han, Hui, Wang, Wen-Yuan, Mao, Bing-Huan (2005). “Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning.” In Huang, De-Shuang, Zhang, Xiao-Ping, Huang, Guang-Bin (eds.), Advances in Intelligent Computing, 878–887. ISBN 978-3-540-31902-3, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/11538059_91")}.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp
,
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
mlr_pipeops
,
mlr_pipeops_adas
,
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_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_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_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
library("mlr3")
# Create example task
data = smotefamily::sample_generator(500, 0.8)
data$result = factor(data$result)
task = TaskClassif$new(id = "example", backend = data, target = "result")
task$head()
table(task$data(cols = "result"))
# Generate synthetic data for minority class
pop = po("blsmote")
bls_result = pop$train(list(task))[[1]]$data()
nrow(bls_result)
table(bls_result$result)
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