Question 1

How to use machinelearn.js in a React app?

Accepted Answer

Install via npm or yarn, import the library in your components, and use models like RandomForestClassifier with fit and predict methods. For browser usage, ensure to handle async operations appropriately in React's lifecycle.

Question 2

machinelearn.js vs TensorFlow.js for beginners?

Accepted Answer

machinelearn.js is better for learning ML basics with a simple, consistent API, while TensorFlow.js offers more advanced features and pre-trained models but has a steeper learning curve. Choose based on educational goals versus production needs.

Question 3

Does machinelearn.js support neural networks?

Accepted Answer

The README doesn't explicitly list neural networks; it focuses on traditional ML algorithms like ensembles. For deep learning, TensorFlow.js might be a better fit, though machinelearn.js could add such features in future updates.

Question 4

How to enable GPU acceleration in machinelearn.js?

Accepted Answer

Install the machinelearn-gpu package and import it in your code, as per the README. This activates GPU support, but note that it adds complexity and may require specific hardware or driver configurations.

Question 5

What algorithms are available in machinelearn.js?

Accepted Answer

Based on the README, it includes models like RandomForestClassifier from ml.ensemble. For a full list, check the repository's source or documentation, which likely covers common supervised and unsupervised learning methods.

Question 6

Can I save and load models in machinelearn.js?

Accepted Answer

Yes, use the toJSON and fromJSON methods available on all models to serialize and deserialize trained states, making it easy to persist models across sessions or share them.

machinelearn.js

What is machinelearn.js?

Overview

Use Cases

Best For

Related Projects

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Open Source Alternative To

Frequently Asked Questions