---
title: "TfidfVectorizer JavaScript Implementation for Browser and Node.js"
description: "Build sparse TF-IDF text features with a scikit-learn-style JavaScript and TypeScript API using @kanaries/ml in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_extraction/tfidfVectorizer"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_extraction/tfidfVectorizer.md"
---
# TfidfVectorizer in JavaScript

## Algorithm overview

`TfidfVectorizer` combines vocabulary learning, count vectorization, inverse-document-frequency weighting, and row normalization. Use it for document classification, similarity, and lightweight semantic search baselines.

## JavaScript implementation

The `@kanaries/ml` implementation takes raw `string[]`, returns a `CSRMatrix`, works in `Pipeline`, and feeds sparse-aware `MultinomialNB` without converting the entire corpus to dense arrays.

## Quick start example

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

const pipeline = new Pipeline({ steps: [
  ['tfidf', new FeatureExtraction.TfidfVectorizer({ ngramRange: [1, 2] })],
  ['nb', new Bayes.MultinomialNB()],
] });
pipeline.fit(['red apple sweet', 'blue sea deep'], [0, 1]);
console.log(pipeline.predict(['deep blue ocean']));
```

The equivalent Python workflow uses `Pipeline([('tfidf', TfidfVectorizer()), ('nb', MultinomialNB())])`.

## Detailed API reference

The constructor accepts every [CountVectorizer](/docs/apis/feature_extraction/countVectorizer.md) option plus every [TfidfTransformer](/docs/apis/feature_extraction/tfidfTransformer.md) option.

- `fit(documents: string[]): void`
- `transform(documents: string[]): CSRMatrix`
- `fitTransform(documents: string[]): CSRMatrix`
- `getFeatureNamesOut(): string[]`
- `vocabulary: ReadonlyMap<string, number>`
- `idf: number[]`

Serialize a fitted instance with `JSON.stringify(vectorizer)` and restore it with the root `loadModel` export.