trainTest: Train / test split
In llama: Leveraging Learning to Automatically Manage Algorithms

Description Usage Arguments Details Value Author(s) See Also Examples

Split a data set into train and test set.

1	trainTest(data, trainpart = 0.6, stratify = FALSE)

`data`	the data to use. The structure returned by `input`.
`trainpart`	the fraction of the data to use for training. Default 0.6.
`stratify`	whether to stratify the folds. Makes really only sense for classification models. Defaults to `FALSE`.

Partitions the data set into training and test set according to the specified fraction. The training and test index sets are added to the original data and returned. If requested, the distribution of the best algorithms in training and test set is approximately the same, i.e. the sets are stratified.

If the data set has train and test partitions already, they are overwritten.

`train`	a (one-element) list of index sets for training.
`test`	a (one-element) list of index sets for testing.
`...`	the original members of `data`. See `input`.

Lars Kotthoff

bsFolds, cvFolds

data(satsolvers)
trainTest = trainTest(satsolvers)

# use 50-50 split instead of 60-40
trainTest1 = trainTest(satsolvers, 0.5)

# stratify
trainTestU = trainTest(satsolvers, stratify=TRUE)