mlr (version 1.1-18)

makeResampleDesc: Create a description object for a resampling strategy.

Description

A description of a resampling algorithm contains all necessary information to create a ResampleInstance, when given the size of the data set.

Usage

makeResampleDesc(method, predict = "test", ...,
    stratify = FALSE)

Value

ResampleDesc.

Details

Object slots: [object Object],[object Object],[object Object]

Repeated cross-validation: Use RepCV. Then you have to set the aggregation function for your preferred performance measure to testgroup.mean via setAggregation. B632 bootstrap: Use Bootstrap for bootstrap and set predict to both. Then you have to set the aggregation function for your preferred performance measure to b632 via setAggregation. B632+ bootstrap: Use Bootstrap for bootstrap and set predict to both. Then you have to set the aggregation function for your preferred performance measure to b632plus via setAggregation. Fixed Holdout set: Use makeFixedHoldoutInstance.

Examples

## Bootstraping
makeResampleDesc("Bootstrap", iters = 10)
makeResampleDesc("Bootstrap", iters = 10, predict = "both")

## Subsampling
makeResampleDesc("Subsample", iters = 10, split = 3/4)
makeResampleDesc("Subsample", iters = 10)

## Holdout respectively test sample estimation
makeResampleDesc("Holdout")