Brookhaven AIMS Tutorial: Random forests

Tuesday 2 May 2023, 12:00 → 13:00 US/Eastern

In 2023, we have expanded the Brookhaven AIMS series to include also hands-on tutorials organized by the BNL Computational Science Initiative, which are aimed at researchers, educators and students new to machine learning and artificial intelligence. The tutorial presentations are designed for a general audience, and minimal prior experience will be required.

 

12:00 → 13:00 Random forests

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

Speaker: Matthew Carbone (Brookhaven National Laboratory)

Random_Forests.mp4