A custom holdout partitions a set of observations into a training set and a test set in a predefined way. This is typically done in order to compare the performance of different predictive algorithms on the same data, as part of a data mining competition or by the researcher who first uses the dataset.
| Folds | 10 |
| Repeats | 1 |
| Holdout percentage | |
| Stratified sampling | false |