Generates a more balanced data set by creating
synthetic instances of the minority class using the SMOTE algorithm.
The algorithm samples for each minority instance a new data point based on the K nearest
neighbors of that data point.
It can only be applied to tasks with purely numeric features.
See smotefamily::SMOTE for details.
Format
R6Class object inheriting from PipeOpTaskPreproc/PipeOp.
Construction
id::character(1)
Identifier of resulting object, default"smote".param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
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.
State
The $state is a named list with the $state elements inherited from PipeOpTaskPreproc.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:
Fields
Only fields inherited from PipeOpTaskPreproc/PipeOp.
Methods
Only methods inherited from PipeOpTaskPreproc/PipeOp.
References
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002). “SMOTE: Synthetic Minority Over-sampling Technique.” Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953 .
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
mlr_pipeops_adas,
mlr_pipeops_blsmote,
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_decode,
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_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_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,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson
Examples
library("mlr3")
# Create example task
data = smotefamily::sample_generator(1000, ratio = 0.80)
data$result = factor(data$result)
task = TaskClassif$new(id = "example", backend = data, target = "result")
task$data()
#> result X1 X2
#> <fctr> <num> <num>
#> 1: p 0.546145996 0.67961492
#> 2: n 0.079991565 0.61547644
#> 3: n 0.643280776 0.03632103
#> 4: n 0.731377352 0.32976618
#> 5: n 0.004454134 0.94679939
#> ---
#> 996: n 0.629311925 0.85093931
#> 997: p 0.607156249 0.52193177
#> 998: n 0.026633458 0.57191021
#> 999: n 0.380717913 0.93177893
#> 1000: n 0.430693496 0.74375332
table(task$data()$result)
#>
#> n p
#> 835 165
# Generate synthetic data for minority class
pop = po("smote")
smotedata = pop$train(list(task))[[1]]$data()
table(smotedata$result)
#>
#> n p
#> 835 825