makeResampleInstance: Instantiates a resampling strategy object.
In guillermozbta/mir: Machine Learning in R

Description Usage Arguments Details Value See Also Examples

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.

1	makeResampleInstance(desc, task, size, coords, ...)

`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 of `size`.
`size`	[`integer`] Size of the data set to resample. Can be used instead of `task`.
`coords`	[`data.frame`] Data.frame with two columns named 'x' and 'y' representing spatial coordinates. Used only if ‘desc = ’SpCV'‘ or 'desc = ’SpRepCV''.
`...`	[any] Passed down to `makeResampleDesc` in case you passed a string in `desc`. Otherwise ignored.

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 specfic iterations 'belong together' (e.g. repeated CV), and it can later be used to aggregate performance values accordingly. Default is 'factor()'.

[ResampleInstance].

Other resample: ResamplePrediction, ResampleResult, addRRMeasure, getRRPredictionList, getRRPredictions, getRRTaskDescription, getRRTaskDesc, makeResampleDesc, resample

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)