Ask the Expert Webinar Series

Tree-Based Machine Learning Methods and Model Interpretability in SAS® Viya®: A Case Study

On-Demand • Cost: Complimentary

About the webinar

Join this webinar to learn how to build machine learning pipelines in SAS Viya.

We will go into detail on key machine learning topics like data preprocessing, variable selection, building tree-based models and model interpretability.

This webinar is perfect for new or experienced SAS users who want to learn more about machine learning. You will learn how to build more complex predictive models (above and beyond linear models).

You will learn:

  • How to use SAS Model Studio to build machine learning pipelines to improve your predictive modeling workflow.
  • How nonlinear models, such as tree-based models, can improve model accuracy as compared to traditional linear models.
  • How to add interpretability plots to black box models and how to use these plots to understand and improve predictive models.

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About the Experts

Ari Zitin
Senior Analytical Training Consultant, SAS

Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low-energy physics data to better understand the neutrino. Zitin taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, he has worked to develop courses that teach how to use Python code to control SAS analytical procedures.

Christa Cody, PhD
Senior Associate Data Scientist, SAS

Christa Cody has a PhD in computer science with a focus on educational tech and machine learning. As a SAS data scientist, she uses data sets, feature engineering and visualizations to provide a variety of stakeholders within SAS insights into the customer’s educational experience. She also works closely with SAS Education Marketing to conduct analytical experiments to solve problems and help users get the most benefit out of their educational content.