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.

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpSmote$new(id = "smote", 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 new values. See`SMOTE()`

.`dup_size`

::`numeric`

Desired times of synthetic minority instances over the original number of majority instances. See`SMOTE()`

.

Only fields inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

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
.

https://mlr3book.mlr-org.com/list-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_scale`

,
`mlr_pipeops_select`

,
`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") # 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 #> 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