---
title: "ClassifierChain in JavaScript and TypeScript"
description: "Model dependencies between multiple binary labels with the @kanaries/ml JavaScript ClassifierChain implementation in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/multioutput/classifierChain"
markdown_url: "https://ml.kanaries.net/docs/apis/multioutput/classifierChain.md"
---
# ClassifierChain in JavaScript

## Algorithm overview

A classifier chain fits one binary classifier per output. Each member sees the original features plus earlier labels, allowing later predictions to depend on earlier outputs rather than assuming label independence.

## JavaScript implementation

Pass any registered single-output classifier to `MultiOutput.ClassifierChain`. A fixed, identity, or seeded random order is supported, and optional cross-validated training extensions reduce target leakage.

## Quick start example

```ts
import { Bayes, MultiOutput } from '@kanaries/ml';

const X = [[0], [1], [2]];
const testX = [[1.5]];
const chain = new MultiOutput.ClassifierChain({
  estimator: new Bayes.GaussianNB(), order: [0, 1],
});
chain.fit(X, [[0, 0], [0, 1], [1, 1]]);
const labels = chain.predict(testX);
console.log(labels);
```

## Detailed API reference

Options: `estimator`, `order?: number[] | 'random'`, `cv?: number | null`, and `randomState`. Methods: `fit(X, Y)`, `predict(X)`, `predictProba(X)`, and exact-match `score(X, Y)`. `chainOrder` and `estimators` return defensive arrays. `predictProba` requires the base classifier to expose probabilities and returns the positive-class probability per output.