---
title: "PLS Regression and CCA in JavaScript"
description: "Model two related data blocks with PLSRegression and canonical correlation analysis in JavaScript or TypeScript."
canonical_url: "https://ml.kanaries.net/docs/apis/cross_decomposition"
markdown_url: "https://ml.kanaries.net/docs/apis/cross_decomposition/index.html.md"
---
# PLS and CCA in JavaScript

## Algorithm overview

Partial least squares finds latent directions that predict a target block, while canonical correlation analysis finds directions with high cross-block correlation. Both help with correlated, multivariate features.

## JavaScript implementation

`PLSRegression` uses mode-A NIPALS with regression deflation. `CCA` uses mode-B weights with canonical deflation.

## Interactive PLS regression playground

Change the latent-component count and data distribution below. The visible first target and holdout metrics come from a live multi-output `CrossDecomposition.PLSRegression` fit.

> The HTML version includes an interactive PLS Regression playground powered by @kanaries/ml. You can change the dataset, noise, and model controls; add observations; and inspect live predictions plus train and holdout metrics. The runnable guide and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/cross_decomposition).

## Quick start

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

const X = [[0, 1], [1, 2], [2, 3], [3, 5]];
const Y = [[1], [2], [4], [7]];
const model = new CrossDecomposition.PLSRegression({ nComponents: 2 });
model.fit(X, Y);
console.log(model.predict([[4, 6]]));
```

## Detailed API reference

Both expose weights, loadings, rotations, iteration counts, `transform`, and `predict`.