---
title: "Linear Regression in JavaScript with @kanaries/ml"
description: "Learn what Linear Regression does, when to use it, and how to run LinearRegression in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/linearRegression"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/linearRegression.md"
---
# Linear Regression in JavaScript

## Algorithm overview

LinearRegression models continuous targets with an interpretable linear relationship between features and outputs.

This algorithm is especially useful when:

- You need a transparent baseline for numeric prediction tasks.
- Feature-target relationships are approximately linear after transformation.
- You want fast training and low-latency inference in JavaScript.

## JavaScript implementation

@kanaries/ml gives JavaScript teams a straightforward linear regression implementation for numeric prediction tasks that need to stay in the application layer. This is useful for browser-side demos, pricing calculators, forecasting helpers, and Node.js APIs that want transparent coefficients instead of opaque black-box behavior.

Because the model is simple and explainable, it fits especially well in product contexts where engineers and stakeholders need to reason about why a prediction changed.

## Interactive linear regression playground

Change the dataset and noise, then click the chart to add your own observation. The fitted line and holdout metrics update immediately using `Linear.LinearRegression` in your browser.

> The HTML version includes an interactive Linear 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/linear/linearRegression).

## Quick start

### LinearRegression: Python and JavaScript / TypeScript

The Python example uses scikit-learn; the TypeScript example uses @kanaries/ml in browser or Node.js runtimes.

#### Python (scikit-learn)

```python
from sklearn.linear_model import LinearRegression

X = [[0], [1], [2], [3]]
y = [1, 3, 5, 7]

reg = LinearRegression()
reg.fit(X, y)
pred = reg.predict([[4], [5]])
```

#### JavaScript / TypeScript (@kanaries/ml)

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

const X = [[0], [1], [2], [3]];
const y = [1, 3, 5, 7];

const reg = new Linear.LinearRegression();
reg.fit(X, y);
const pred = reg.predict([[4], [5]]);
```

### Quick JavaScript example

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

const X = [[0], [1], [2], [3]];
const y = [1, 3, 5, 7];

const reg = new Linear.LinearRegression();
reg.fit(X, y);
const pred = reg.predict([[4], [5]]);
console.log(pred);
```

## Detailed API reference

```ts
constructor()
```

This class implements ordinary least squares linear regression. It estimates
coefficients for a linear model by minimizing the squared error between
predicted and actual values.

### Methods

- `fit(X: number[][], Y: number[]): void`
- `predict(X: number[][]): number[]`

#### fit

- `X` - Feature matrix of shape `[nSamples, nFeatures]`.
- `Y` - Target values of length `nSamples`.

#### predict

- `X` - Feature matrix for which to compute predictions.

### Implementation workflow

1. Prepare numeric features and split into train/validation sets.
2. Fit the model and inspect residual patterns for systematic errors.
3. Iterate on feature engineering when residuals show non-linear structure.

### JavaScript deployment notes

- Use linear regression as a baseline before moving to more complex non-linear models.
- Inspect residuals to decide whether feature engineering or a different model family is needed.
- This estimator is a strong fit for small to medium tabular problems where interpretability matters.