---
title: "TfidfTransformer in JavaScript and TypeScript"
description: "Weight dense or CSR term-count matrices with TF-IDF in browser or Node.js using the @kanaries/ml JavaScript implementation."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_extraction/tfidfTransformer"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_extraction/tfidfTransformer.md"
---
# TfidfTransformer in JavaScript

## Algorithm overview

TF-IDF reduces the influence of terms that occur in many documents while preserving terms that distinguish individual documents. `TfidfTransformer` is useful when counts already come from another source.

## JavaScript implementation

`@kanaries/ml` accepts either `number[][]` or `CSRMatrix` counts and returns a sparse CSR result. This keeps text preprocessing memory-efficient in browser and Node.js pipelines.

## Quick start example

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

const tfidf = new FeatureExtraction.TfidfTransformer({ norm: 'l2' });
const weighted = tfidf.fitTransform([[2, 0, 1], [0, 1, 1]]);
console.log(weighted.toDense());
```

## Detailed API reference

```ts
new FeatureExtraction.TfidfTransformer({
  norm?: 'l1' | 'l2' | null; // 'l2'
  useIdf?: boolean;          // true
  smoothIdf?: boolean;       // true
  sublinearTf?: boolean;     // false
})
```

- `fit(X): void` learns inverse-document-frequency weights.
- `transform(X): CSRMatrix` applies term-frequency scaling, IDF, and normalization.
- `fitTransform(X): CSRMatrix` combines both operations.
- `idf: number[]` returns a defensive copy of the learned weights.

Inputs must contain non-negative term counts and use the fitted feature count.