mlr_pipeops_adas | R Documentation |
Generates a more balanced data set by creating synthetic instances of the minority classes using the ADASYN algorithm.
The algorithm generates for each minority instance new data points based on its K
nearest neighbors and the difficulty of learning for that data point.
It can only be applied to tasks with numeric features that have no missing values.
See smotefamily::ADAS
for details.
R6Class
object inheriting from PipeOpTaskPreproc
/PipeOp
.
PipeOpADAS$new(id = "adas", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "adas"
.
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 $state
elements inherited from PipeOpTaskPreproc
.
The parameters are the parameters inherited from PipeOpTaskPreproc
, as well as:
K
:: numeric(1)
The number of nearest neighbors used for sampling new values. Default is 5
.
See ADAS()
.
If a target level is unobserved during training, no synthetic data points will be generated for that class. No error is raised; the unobserved class is simply ignored.
Only fields inherited from PipeOp
.
Only methods inherited from PipeOpTaskPreproc
/PipeOp
.
He H, Bai Y, Garcia, A. E, Li S (2008). “ADASYN: Adaptive synthetic sampling approach for imbalanced learning.” In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322-1328. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/IJCNN.2008.4633969")}.
https://mlr-org.com/pipeops.html
Other PipeOps:
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,
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,
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,
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,
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,
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,
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library("mlr3")
# Create example task
data = data.frame(
target = factor(sample(c("c1", "c2"), size = 300, replace = TRUE, prob = c(0.1, 0.9))),
x1 = rnorm(300),
x2 = rnorm(300)
)
task = TaskClassif$new(id = "example", backend = data, target = "target")
task$head()
table(task$data(cols = "target"))
# Generate synthetic data for minority class
pop = po("adas")
adas_result = pop$train(list(task))[[1]]$data()
nrow(adas_result)
table(adas_result$target)
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