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K-Fold Cross-Validation Indices

Generate the train and validation index splits for K-Fold cross-validation.

Given $n$ data points and $k$ folds, divide indices $[0, 1, ..., n-1]$ into $k$ approximately equal folds. For each fold $i$ , use fold $i$ as validation and the remaining folds as training.

Return a list of $k$ tuples: [(train_indices, val_indices), ...] where each set of indices is a sorted list.

Split the indices sequentially (first n//k go to fold 0, next to fold 1, etc.). If $n$ is not evenly divisible, the first $n \mod k$ folds get one extra element.

Example:

Input:

n = 5, k = 3

Output:

[([2, 3, 4], [0, 1]), ([0, 1, 4], [2, 3]), ([0, 1, 2, 3], [4])]

Reasoning:

First, we divide the $n = 5$ data points into $k = 3$ folds. Since $5$ is not evenly divisible by $3$ , we calculate the size of each fold as $\frac{5}{3} = 1$ with a remainder of $2$ . This means the first $2$ folds will have $2$ elements, and the last fold will have $1$ element.
We then split the indices $[0, 1, ..., 4]$ into $3$ folds: fold $0$ gets indices $[0, 1]$ , fold $1$ gets indices $[2, 3]$ , and fold $2$ gets index $[4]$ .
For each fold $i$ , we use the indices in fold $i$ as validation and the remaining folds as training. So for fold $0$ , the validation indices are $[0, 1]$ and the training indices are $[2, 3, 4]$ . For fold $1$ , the validation indices are $[2, 3]$ and the training indices are $[0, 1, 4]$ . For fold $2$ , the validation indices are $[4]$ and the training indices are $[0, 1, 2, 3]$ .
The final output is a list of these training and validation index splits: [([2, 3, 4], [0, 1]), ([0, 1, 4], [2, 3]), ([0, 1, 2, 3], [4])]

Constraints:

n >= k >= 2
Return list of (train_indices, val_indices) tuples
Indices in each list should be sorted
Folds are sequential (not shuffled)

Editor

Python 3.13.1

Test Results

0/0

Run code to see test results.

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