Linear Regression in JavaScript with @kanaries/ml

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.

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.

@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.

Quick start

LinearRegression in Python vs JavaScript / TypeScript

If you searched for "LinearRegression in JavaScript" or "LinearRegression in TypeScript", this section maps the familiar scikit-learn call to the equivalent @kanaries/ml usage for browser and Node.js runtimes.

Python

scikit-learn

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

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

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]]);

Detailed API reference

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

Prepare numeric features and split into train/validation sets.
Fit the model and inspect residual patterns for systematic errors.
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.

Linear Regression