---
title: "Group-Aware Cross-Validation Splitters in JavaScript"
description: "Prevent entity leakage with GroupShuffleSplit and StratifiedGroupKFold JavaScript implementations from @kanaries/ml in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/utils/groupSplitters"
markdown_url: "https://ml.kanaries.net/docs/apis/utils/groupSplitters.md"
---
# Group-Aware Cross-Validation in JavaScript

## Algorithm overview

Random sample splits leak information when multiple rows belong to the same user, patient, session, or device. Group-aware splitters keep each group entirely on one side of a fold; stratified group folds additionally balance class proportions.

## JavaScript implementation

`@kanaries/ml` exposes both splitters through `utils.ModelSelection`, so browser experiments and Node.js evaluation jobs can use leakage-resistant indices within the same JS workflow.

## Quick start example

```ts
import { utils } from '@kanaries/ml';

const X = [[0], [1], [2], [3], [4], [5]];
const y = [0, 0, 1, 1, 0, 1];
const patientIds = ['a', 'a', 'b', 'b', 'c', 'c'];
const folds = new utils.ModelSelection.StratifiedGroupKFold({
  nSplits: 3, shuffle: true, randomState: 42,
}).split(X, y, patientIds);
console.log(folds);
```

## Detailed API reference

```ts
new utils.ModelSelection.GroupShuffleSplit({
  nSplits?: number; testSize?: number; trainSize?: number; randomState?: number;
})

new utils.ModelSelection.StratifiedGroupKFold({
  nSplits?: number; shuffle?: boolean; randomState?: number;
})
```

Both expose `split(X, y?, groups): FoldIndices[]`. `GroupShuffleSplit` samples unique groups and interprets fractional sizes against the group count, not the row count. `StratifiedGroupKFold` requires `y` and greedily balances per-class proportions while never splitting a group.