---
title: "Univariate Feature Scores in JavaScript and TypeScript"
description: "Rank classification and regression features with chi-square, ANOVA F, and k-nearest-neighbor mutual information JavaScript functions from @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/feature_selection/univariate"
markdown_url: "https://ml.kanaries.net/docs/apis/feature_selection/univariate.md"
---
# Univariate feature scores in JavaScript

## Algorithm overview

Univariate scores examine one feature at a time. Chi-square is suited to non-negative count features, ANOVA F measures class separation, and mutual information detects more general nonlinear dependence using discrete counts or k-nearest-neighbor entropy estimates.

## JavaScript implementation

`@kanaries/ml` makes these score functions available in browser and Node.js code through `FeatureSelection`, including seeded jitter for repeated continuous values.

## Quick start example

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

const X = [[0, 1], [1, 0], [5, 1], [6, 0]];
const y = [0, 0, 1, 1];
const [fScores, pValues] = FeatureSelection.fClassif(X, y);
const information = FeatureSelection.mutualInfoClassif(X, y, { randomState: 42 });
console.log({ fScores, pValues, information });
```

## Detailed API reference

- `chi2(X, y)` and `fClassif(X, y)` return `[scores, pValues]`.
- `mutualInfoClassif(X, y, options?)` and `mutualInfoRegression(X, y, options?)` return one non-negative score per feature.
- Mutual-information options are `discreteFeatures?: boolean | boolean[]`, `nNeighbors?: number` (default `3`), and `randomState?: number`.