Machine Learning in JavaScript — scikit-learn style
A lightweight JS machine learning library for browser and Node.js. Familiar scikit-learn API, zero-install demos, production-ready builds.
Why JavaScript for ML?
Run models in the browser
Ship zero-backend ML experiences with full client-side inference and privacy by default.
One language front to back
Build training pipelines and production inference with Node.js and modern frameworks.
Interactive ML UIs
Create exploratory dashboards that react instantly without server round-trips.
Tiny bundles & WASM-ready
Tree-shakable ESM builds, typed arrays and Web Worker friendly execution.
What you can build
Tabular
Client-side churn prediction, lead scoring, and data capture scoring widgets.
Text
Intent detection, TF–IDF pipelines, and simple classifiers entirely in JS.
Clustering & DR
k-means, PCA, and dimensionality reduction for visualization dashboards.
Time series
Prototype forecasts in Node.js with classical regression and smoothing.
Key features
Familiar API
Just like scikit-learn: fit, predict, pipelines, transformers, and metrics.
Browser & Node support
ESM-first distribution runs anywhere JavaScript does.
Fast numerics
Typed arrays and WASM-friendly architecture keep memory low and performance high.
Model persistence
Serialize trained pipelines to JSON and reload them in any runtime.
Docs for every algorithm
Runnable guides for each estimator shorten onboarding time.
Tiny footprint
Import only what you need with tree-shakable modules.
Quick start
Install
yarn add @kanaries/mlNode.js
import { Linear, utils } from '@kanaries/ml';
const X = [
[5.1, 3.5],
[4.9, 3.0],
[7.0, 3.2],
];
const y = [0, 0, 1];
const scaler = new utils.Preprocessing.StandardScaler();
const Xs = scaler.fitTransform(X);
const clf = new Linear.LogisticRegression({ maxIter: 200 });
clf.fit(Xs, y);
console.log(clf.predict([[6.1, 3.1]]));
Use from a CDN (browser)
<script type="module">
import { KMeans } from 'https://cdn.skypack.dev/@kanaries/ml';
const km = new KMeans({ k: 3 }).fit([
[1, 1],
[1.2, 1.1],
[5, 5],
]);
console.log(km.predict([[1.1, 1]]));
</script>Implemented modules and API quick links
The links below go directly to existing documentation pages so you can quickly inspect available APIs and algorithms.
Tree & Ensemble
Neighbors & Clustering
Decomposition & Manifold
Benchmarks & footprint
Core bundle < 50 kB gzipped; individual algorithms typically stay below 15 kB. Use Web Workers for CPU-heavy tasks and opt into the WASM build for additional throughput.
We are prioritizing API coverage and practical examples first. For implementation details and usage patterns, jump to the full API reference.
Open full API reference →How it works
Prepare data
Clean and transform with scalers, encoders, and pipeline helpers.
Fit a model
Use the sklearn-style API to train estimators on typed arrays or plain arrays.
Deploy anywhere
Persist to JSON, load in browsers or Node.js, and call predict in milliseconds.
Compare with scikit-learn
@kanaries/ml keeps familiar `fit`/`predict` patterns and estimator-style classes. Use the docs index to find module-level references and examples quickly.
Open docs index →FAQ
Is JavaScript fast enough for ML?
Yes—for small to medium datasets and interactive experiences, @kanaries/ml delivers responsive inference. Use Web Workers to keep UIs snappy and review our benchmarks for throughput guidance.
Does it run fully in the browser?
Absolutely. All supported algorithms can execute in modern browsers and in Node.js environments.
Can I save and load models?
Every estimator can serialize parameters to JSON. Reload them in any JS runtime and resume predictions instantly.
How close is the API to scikit-learn?
We follow scikit-learn naming and options wherever practical. The migration guide highlights the rare differences.
How big is the bundle?
The core stays well below typical UI bundle budgets and each algorithm tree-shakes to just the code you import. See the benchmarks section for the latest size numbers.
Does it support Web Workers or WASM?
Yes. The runtime is designed for off-main-thread execution and plays nicely with WASM-backed kernels.
Join the community
Build ML in JavaScript today
Deploy machine learning models anywhere JavaScript runs with @kanaries/ml.