---
title: "BaggingClassifier in JavaScript with @kanaries/ml"
description: "Train bootstrap classifier ensembles with the BaggingClassifier JavaScript and TypeScript implementation in @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/ensemble/baggingClassifier"
markdown_url: "https://ml.kanaries.net/docs/apis/ensemble/baggingClassifier.md"
---
# BaggingClassifier in JavaScript

## Algorithm overview

BaggingClassifier trains multiple classifiers on resampled datasets and predicts by vote. It is useful for reducing variance in unstable base estimators such as decision trees.

## JavaScript implementation

`@kanaries/ml` implements `Ensemble.BaggingClassifier` with a default decision-tree base estimator. You can also pass an `estimatorFactory` for custom estimators that implement `fit` and `predict`.

## Quick start example

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

const clf = new Ensemble.BaggingClassifier({
  nEstimators: 20,
  maxSamples: 3,
  randomState: 12,
});

clf.fit([[0, 0], [0, 1], [3, 3], [4, 3]], [0, 0, 1, 1]);
const pred = clf.predict([[1, 1], [4, 4]]);
console.log(pred);
```

## Detailed API reference

```ts
new Ensemble.BaggingClassifier(props?: {
  nEstimators?: number;
  maxSamples?: number;
  bootstrap?: boolean;
  randomState?: number;
  estimatorFactory?: (seed?: number) => {
    fit(X: number[][], y: number[]): void;
    predict(X: number[][]): number[];
  };
  // plus DecisionTreeClassifier options when using the default estimator
})
```

Methods:

- `fit(trainX: number[][], trainY: number[]): void`
- `predict(testX: number[][]): number[]`

Defaults are `nEstimators: 10` and `bootstrap: true`.