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
. Instead of a Task
, a
TaskClassif
is used as input and output during training and prediction.
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(1)
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
.
If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.
Only fields inherited from PipeOp
.
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
Han H, Wang W, Mao B (2005). “Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning.” In Huang D, Zhang X, Huang G (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
,
PipeOpEncodePL
,
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
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,
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,
mlr_pipeops_classifavg
,
mlr_pipeops_classweights
,
mlr_pipeops_colapply
,
mlr_pipeops_collapsefactors
,
mlr_pipeops_colroles
,
mlr_pipeops_copy
,
mlr_pipeops_datefeatures
,
mlr_pipeops_decode
,
mlr_pipeops_encode
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_encodeplquantiles
,
mlr_pipeops_encodepltree
,
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_learner_pi_cvplus
,
mlr_pipeops_learner_quantiles
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_mutate
,
mlr_pipeops_nearmiss
,
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_tomek
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
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,
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,
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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