# datatable.models.kfold()¶

Perform k-fold split of data with nrows rows into nsplits train/test subsets. The dataset itself is not passed to this function: it is sufficient to know only the number of rows in order to decide how the data should be split.

The range [0; nrows) is split into nsplits approximately equal parts, i.e. folds, and then each i-th split will use the i-th fold as a test part, and all the remaining rows as the train part. Thus, i-th split is comprised of:

• train rows: [0; i*nrows/nsplits) + [(i+1)*nrows/nsplits; nrows);

• test rows: [i*nrows/nsplits; (i+1)*nrows/nsplits).

where integer division is assumed.

## Parameters¶

nrows
int

The number of rows in the frame that is going to be split.

nsplits
int

Number of folds, must be at least 2, but not larger than nrows.

return
List[Tuple]

This function returns a list of nsplits tuples (train_rows, test_rows), where each component of the tuple is a rows selector that can be applied to any frame with nrows rows to select the desired folds. Some of these row selectors will be simple python ranges, others will be single-column Frame objects.

kfold_random() – Perform randomized k-fold split.