Bear Machine Learning

Bear

What is Bear?

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

Bear finds statistically significant dependencies between variables to build forests of piecewise constant models.

It then models the residuals of each forest using other variables to build new forests, and so on.

Bear is thus similar to random forests, but with a different 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.

Simple tutorial shows how Bear can model bivariate data that linear regression models well.
Intermediate tutorial describes Bear’s model-building process with nonlinear bivariate and multivariate data.
Advanced tutorial describes the model weighting process and other details.

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 License.