makeResampleInstance

0th

Percentile

Instantiates a resampling strategy object.

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 | 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.
...
[any] Passed down to makeResampleDesc in case you passed a string in desc. 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 specfic iterations 'belong together' (e.g. repeated CV), and it can later be used to aggregate performance values accordingly. Default is 'factor()'.

Value

[ResampleInstance].

See Also

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

Aliases
  • makeResampleInstance
  • ResampleInstance
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)
Documentation reproduced from package mlr, version 2.10, License: BSD_2_clause + file LICENSE

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