Bear: a new machine learning engine

Bear

NOTE

I am currently rebuilding Bear to properly implement leave-one-out cross validation. ETA is late 2025. Apologies.

Bear is a new free and open source machine learning engine that I have developed as a personal hobby since 2008.

Bear finds statistically significant dependencies between features and labels to build forests of piecewise constant models.

Leave-one-out cross-validation is baked in, automatic, and free, for any arbitrarily large sample size.

Bear is thus a Monte Carlo engine similar to random forests, but with a different core philosophy and algorithm.

Its fundamentally frequentist approach automatically avoids overfitting. It learns efficiently without needing neural networks.

Bear won’t “replace” existing ML or AI systems. It’s a machine learning engine that might in future be dropped into existing systems.

My simple tutorial introduces you to Bear step by step with very simple examples.
My intermediate tutorial shows you how Bear performs on slightly more complex datasets.
In my advanced tutorial I describe some aspects of how Bear works in more detail.

A quick guide to building and running my free and open source ANSI C implementation of Bear is here.

These Bear pages describe personal hobby research that I have undertaken since 2008.

All opinions are mine alone. All code is from my personal codebase, supplied under the MIT-0 License.