---
title: "Isolation Forest in JavaScript with @kanaries/ml"
description: "Learn what Isolation Forest does, when to use it, and how to run IsolationForest in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/ensemble/iforest"
markdown_url: "https://ml.kanaries.net/docs/apis/ensemble/iforest.md"
---
# Isolation Forest in JavaScript

## Algorithm overview

IsolationForest detects anomalies by isolating rare points with shorter path lengths in random partition trees.

This algorithm is especially useful when:

- You have mostly normal behavior with relatively few outliers.
- Labeling anomalies is expensive or unavailable.
- You need near-real-time anomaly scoring in browser or Node.js.

## JavaScript implementation

@kanaries/ml provides Isolation Forest in JavaScript for anomaly detection features that need to run inside a product, a browser-based monitoring view, or a Node.js scoring service. That is useful when you want outlier detection near the application layer, for example in fraud heuristics, telemetry triage, or unusual-behavior alerts.

Keeping anomaly scoring in JS makes it easier to connect feature extraction, threshold logic, and downstream product actions without introducing a Python serving dependency.

## Quick start

### IsolationForest: 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.ensemble import IsolationForest

X = [[0, 0], [0.1, 0.2], [0.2, 0.1], [8, 8]]

clf = IsolationForest(n_estimators=50, contamination=0.25, random_state=0)
clf.fit(X)
pred = clf.predict(X)
```

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

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

const X = [[0, 0], [0.1, 0.2], [0.2, 0.1], [8, 8]];

const clf = new Ensemble.IsolationForest(256, 50, 0.25);
clf.fit(X);
const pred = clf.predict(X);
```

### Quick JavaScript example

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

const X = [[0, 0], [0.1, 0.2], [0.2, 0.1], [8, 8]];

const clf = new Ensemble.IsolationForest(256, 50, 0.25);
clf.fit(X);
const pred = clf.predict(X);
console.log(pred);
```

## Detailed API reference

```ts
constructor(subsampling_size: number = 256, tree_num: number = 100, contamination: 'auto' | number = 'auto')
```

### Parameters

| name              | type             | default | description                               |
| ----------------- | ---------------- | ------- | ----------------------------------------- |
| subsampling\_size | number           | 256     | Number of samples used to build each tree |
| tree\_num         | number           | 100     | Number of isolation trees in the forest   |
| contamination     | 'auto' \| number | 'auto'  | Expected proportion of outliers           |

### Algorithm

IsolationForest randomly splits features to isolate samples. Points that can be isolated with fewer splits are considered anomalies.

### Methods

- `fit(samplesX: number[][]): void`
- `predict(samplesX: number[][]): (0|1)[]`

### Implementation workflow

1. Train on representative mostly-normal historical samples.
2. Predict anomaly labels or scores on incoming events.
3. Tune contamination and decision thresholds using alert precision targets.

### JavaScript deployment notes

- Train on representative mostly-normal data so anomaly scores reflect real deviations.
- Treat contamination and alert thresholds as product decisions, not just model defaults.
- For real-time scoring, keep feature generation and model prediction in the same Node.js pipeline when possible.