Chunks its input into
Tasks during training, and
simply passes on the input during
outnum times during prediction.
R6Class object inheriting from
PipeOpChunk$new(outnum, id = "chunk", param_vals = list())
Number of output channels, and therefore number of chunks created.
Identifier of resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
PipeOpChunk has one input channel named
"input", taking a
Task both during training and prediction.
PipeOpChunk has multiple output channels depending on the
options construction argument, named
All output channels produce (respectively disjoint, random) subsets of the input
Task during training, and
pass on the original
Task during prediction.
$state is left empty (
Should the data be shuffled before chunking? Initialized to
Only fields inherited from
Only methods inherited from
library("mlr3") task = tsk("wine") opc = mlr_pipeops$get("chunk", 2) # watch the row number: 89 during training (task is chunked)... opc$train(list(task)) # ... 178 during predict (task is copied) opc$predict(list(task))
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