Splits a classification Task into several binary classification Tasks to perform "One vs. Rest" classification. This works in combination
For each target level a new binary classification Task is constructed with
the respective target level being the positive class and all other target levels being the
new negative class
PipeOp creates a
Multiplicity, which means that subsequent
PipeOps are executed
multiple times, once for each created binary Task, until a
Multiplicity is currently an experimental features and the implementation or UI
R6Class inheriting from
PipeOpOVRSplit$new(id = "ovrsplit", param_vals = list())
Identifier of the resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
PipeOpOVRSplit has one input channel named
"input" taking a
both during training and prediction.
PipeOpOVRSplit has one output channel named
"output" returning a
TaskClassifs both during training and prediction, i.e., the newly
constructed binary classification Tasks.
$state contains the original target levels of the
PipeOpOVRSplit has no parameters.
The original target levels stored in the
$state are also used during prediction when creating the new
binary classification Tasks.
The names of the element of the output
Multiplicity are given by the levels of the target.
If a target level
"rest" is present in the input
negative class will be labeled as
"rest." (using as many "."' postfixes needed to yield a
Should be used in combination with
Only fields inherited from
Only methods inherited from
Other Multiplicity PipeOps:
Other Experimental Features:
library(mlr3) task = tsk("iris") po = po("ovrsplit") po$train(list(task)) po$predict(list(task))
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