Random forests

• Matthew Carbone (Brookhaven National Laboratory)

**Abstract**: The random forest (which is formally an ensemble of decision trees) is a conceptually straightforward machine learning model that can be used "out of the box" with sensible defaults on many different types of classification and regression problems. In this tutorial, you will learn how random forests work, when they are useful, and see multiple examples of random forests in action. These examples include a community-accepted datasets (Palmer Penguins) as well as an application to X-ray absorption spectroscopy (Torrisi, et al: https://doi.org/10.1038/s41524-020-00376-6). **GitHub link**: https://github.com/matthewcarbone/AIML-tutorials