---
title: "Feature Hashing and Dictionary Vectorization in JavaScript"
description: "Use HashingVectorizer, DictVectorizer, and FeatureHasher for bounded-memory JavaScript and TypeScript feature extraction."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_extraction/hashingAndDictionaries"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_extraction/hashingAndDictionaries.md"
---
# Feature hashing in JavaScript

## Algorithm overview

Hashing maps features into a fixed-width sparse matrix without storing a vocabulary. Dictionary vectorization instead learns explicit names, which is preferable when interpretability matters.

## JavaScript implementation

`HashingVectorizer` tokenizes text, `FeatureHasher` accepts dictionaries, pairs, or strings, and `DictVectorizer` expands numeric and categorical dictionary values.

## Quick start

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

const documents = ['red fox', 'blue fox'];
const X = new FeatureExtraction.HashingVectorizer({ nFeatures: 4096 }).fitTransform(documents);
console.log(X.shape);
```

## Detailed API reference

Both hashers use sklearn-compatible MurmurHash3 indices and signed collisions. `DictVectorizer` exposes `vocabulary`, `getFeatureNamesOut()`, `inverseTransform()`, and `restrict()`, and can return CSR or dense output.