---
title: "Extra Tree Regressor in JavaScript with @kanaries/ml"
description: "Learn what Extra Tree Regressor does, when to use it, and how to run ExtraTreeRegressor in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/tree/extraTreeRegressor"
markdown_url: "https://ml.kanaries.net/docs/apis/tree/extraTreeRegressor.md"
---
# Extra Tree Regressor in JavaScript

## Algorithm overview

ExtraTreeRegressor uses randomized splits for regression to reduce variance and capture non-linear structure efficiently.

This algorithm is especially useful when:

- DecisionTreeRegressor is too sensitive to small data perturbations.
- You need robust tree-style regression with limited tuning overhead.
- Non-linear relationships dominate your numeric prediction task.

## JavaScript implementation

@kanaries/ml provides Extra Tree regression in JavaScript for non-linear tabular prediction with randomized splitting behavior. This can be useful when you want a lightweight tree regressor that differs from a standard decision tree in how it explores split candidates.

For TypeScript-based experimentation, it is a convenient way to compare deterministic and randomized tree regression strategies without switching runtimes.

## Interactive Extra Tree playground

Tune the depth and minimum split size, add your own observations, and reroll the randomized thresholds. The chart and tree diagram are trained live with `Tree.ExtraTreeRegressor`, so every step in the blue prediction line comes from an actual leaf.

> The HTML version includes an interactive Extra Tree Regressor playground. You can tune the tree depth, split constraints, dataset noise, and criterion; add observations; and inspect the fitted predictions and tree structure. The guide, runnable example, and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/tree/extraTreeRegressor).

## Quick start

### ExtraTreeRegressor: Python and JavaScript / TypeScript

The Python example uses scikit-learn; the TypeScript example uses @kanaries/ml in browser or Node.js runtimes.

#### Python (scikit-learn)

```python
from sklearn.tree import ExtraTreeRegressor

X = [[0], [1], [2], [3]]
y = [1.0, 2.0, 3.1, 4.1]

reg = ExtraTreeRegressor(max_depth=3, random_state=0)
reg.fit(X, y)
pred = reg.predict([[1.5], [2.5]])
```

#### JavaScript / TypeScript (@kanaries/ml)

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

const X = [[0], [1], [2], [3]];
const y = [1.0, 2.0, 3.1, 4.1];

const reg = new Tree.ExtraTreeRegressor({ max_depth: 3 });
reg.fit(X, y);
const pred = reg.predict([[1.5], [2.5]]);
```

### Quick JavaScript example

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

const X = [[0], [1], [2], [3]];
const y = [1.0, 2.0, 3.1, 4.1];

const reg = new Tree.ExtraTreeRegressor({ max_depth: 3 });
reg.fit(X, y);
const pred = reg.predict([[1.5], [2.5]]);
console.log(pred);
```

## Detailed API reference

```ts
interface ExtraTreeRegressorProps {
    max_depth?: number;
    min_samples_split?: number;
    splitter?: 'random';
    max_features?: number | 'sqrt' | 'log2';
}

constructor(props: ExtraTreeRegressorProps = {})
```

### Implementation workflow

1. Fit with baseline constraints and inspect holdout error metrics.
2. Benchmark against linear and decision tree baselines.
3. Tune depth/min-sample controls for stable generalization.

### JavaScript deployment notes

- Use Extra Tree regression when you want a randomized tree baseline for structured regression tasks.
- Compare it against the standard decision tree regressor to understand the stability-versus-variance tradeoff.
- It is particularly useful as a stepping stone toward ensemble-style tree modeling.