---
title: "ExtraTreesClassifier in JavaScript with @kanaries/ml"
description: "Train extremely randomized classification trees in JavaScript or TypeScript with ExtraTreesClassifier from @kanaries/ml for browser and Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/ensemble/extraTreesClassifier"
markdown_url: "https://ml.kanaries.net/docs/apis/ensemble/extraTreesClassifier.md"
---
# ExtraTreesClassifier in JavaScript

## Algorithm overview

Extremely randomized trees combine many trees whose split thresholds are sampled randomly. The added randomization can reduce variance while retaining nonlinear decision boundaries.

## JavaScript implementation

`Ensemble.ExtraTreesClassifier` trains independent `ExtraTreeClassifier` members, averages their leaf class probabilities, and predicts the highest-probability class. It supports deterministic seeds and exposes averaged feature importances.

## Quick start example

```ts
import { Ensemble } from '@kanaries/ml';
const model = new Ensemble.ExtraTreesClassifier({ nEstimators: 100, max_features: 'sqrt', randomState: 42 });
model.fit([[0, 0], [0, 1], [3, 3], [4, 3]], [0, 0, 1, 1]);
console.log(model.predict([[.2, .1], [3.5, 3]]), model.featureImportances);
```

## Detailed API reference

Options include `nEstimators?`, `bootstrap?`, `randomState?`, `max_features?` (integer, fraction, `sqrt`, `log2`, or `all`), and the `ExtraTreeClassifier` depth/split options. Methods are `fit(X, y)`, `predictProba(X)`, and `predict(X)`; classification averages leaf probabilities before taking the highest-probability class. `classes` and `featureImportances` are available after fitting.