---
title: "CountVectorizer in JavaScript and TypeScript"
description: "Tokenize browser or Node.js text into a memory-efficient CSR count matrix with the @kanaries/ml JavaScript CountVectorizer implementation."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_extraction/countVectorizer"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_extraction/countVectorizer.md"
---
# CountVectorizer in JavaScript

## Algorithm overview

`CountVectorizer` learns a vocabulary from documents and represents each document by token or n-gram frequency. It is a strong baseline for text classification, search features, and document analytics.

## JavaScript implementation

The `@kanaries/ml` implementation accepts `string[]` and returns `CSRMatrix`, avoiding a dense allocation when the vocabulary is large. The fitted vectorizer can run in a browser or Node.js and can be serialized with the standard model codec.

## Quick start example

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

const vectorizer = new FeatureExtraction.CountVectorizer({
  ngramRange: [1, 2],
  minDf: 1,
  stopWords: ['the'],
});
const counts = vectorizer.fitTransform(['the red fox', 'the red bird']);
console.log(vectorizer.getFeatureNamesOut());
console.log(counts.toDense());
```

Python uses `CountVectorizer(ngram_range=(1, 2), stop_words=['the'])`; JavaScript uses camel-case option names and the same `fit`/`transform` workflow.

## Detailed API reference

```ts
new FeatureExtraction.CountVectorizer({
  lowercase?: boolean;              // true
  stopWords?: string[];             // []
  ngramRange?: [number, number];     // [1, 1]
  minDf?: number;                    // 1; values below 1 are proportions
  maxDf?: number;                    // 1; values at or below 1 are proportions
  maxFeatures?: number;
  binary?: boolean;                  // false
  vocabulary?: Record<string, number> | Map<string, number>;
})
```

- `fit(documents: string[]): void` learns the vocabulary.
- `transform(documents: string[]): CSRMatrix` produces sparse counts.
- `fitTransform(documents: string[]): CSRMatrix` combines both operations.
- `getFeatureNamesOut(): string[]` returns terms in column order.
- `vocabulary` returns a defensive `ReadonlyMap` copy.

Because JavaScript has one numeric type, `maxDf: 1` follows the proportional/default interpretation; use an integer greater than one for an absolute document threshold.