makeResampleInstance {mlr} | R Documentation |
Instantiates a resampling strategy object.
Description
This class encapsulates training and test sets generated from the data set for a number of iterations. It mainly stores a set of integer vectors indicating the training and test examples for each iteration.
Usage
makeResampleInstance(desc, task, size, ...)
Arguments
desc |
(ResampleDesc | |
task |
(Task) |
size |
(integer) |
... |
(any) |
Details
Object slots:
- desc (ResampleDesc)
See argument.
- size (
integer(1)
) See argument.
- train.inds (list of integer)
List of of training indices for all iterations.
- test.inds (list of integer)
List of of test indices for all iterations.
- group (factor)
Optional grouping of resampling iterations. This encodes whether specific iterations 'belong together' (e.g. repeated CV), and it can later be used to aggregate performance values accordingly. Default is 'factor()'.
Value
See Also
Other resample:
ResamplePrediction
,
ResampleResult
,
addRRMeasure()
,
getRRPredictionList()
,
getRRPredictions()
,
getRRTaskDesc()
,
getRRTaskDescription()
,
makeResampleDesc()
,
resample()
Examples
rdesc = makeResampleDesc("Bootstrap", iters = 10)
rin = makeResampleInstance(rdesc, task = iris.task)
rdesc = makeResampleDesc("CV", iters = 50)
rin = makeResampleInstance(rdesc, size = nrow(iris))
rin = makeResampleInstance("CV", iters = 10, task = iris.task)