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.
Arguments
- desc
(ResampleDesc |
character(1))
Resampling description object or name of resampling strategy. In the latter case makeResampleDesc will be called internally on the string.- task
(Task)
Data of task to resample from. Prefer to pass this instead ofsize.- size
(integer)
Size of the data set to resample. Can be used instead oftask.- ...
(any)
Passed down to makeResampleDesc in case you passed a string indesc. Otherwise ignored.
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()'.
See also
Other resample:
ResamplePrediction,
ResampleResult,
addRRMeasure(),
getRRPredictionList(),
getRRPredictions(),
getRRTaskDescription(),
getRRTaskDesc(),
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)