On-Demand Webinar

The Data Scientist Learning Journey: Tree-Based Machine Learning Models – A Discussion and Useful Tips

This webinar series is designed for individuals who want to elevate their data science skills using SAS® and keep pace with the most innovative technology.

The Data Scientist Learning Journey

About the webinar

Decision trees are among the most popular workhorses of the predictive modeling world.

But what’s more interesting than a tree? A whole forest!

Our special guest for this month is Sharad Saxena, author of Tree-Based Machine Learning Methods in SAS® Viya®.

During this webinar, Cat and Sharad will discuss several different types of tree-based models, from the most-used to offbeat types of models you might not know about.

Why attend:

  •  Learn about different types of tree-based models.
  • Discover how you can tweak your modeling approach.
  • Join an engaging discussion where you’re sure to learn something new.

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About the Experts


Catherine Truxillo
Director of Analytical Education, SAS

Catherine Truxillo has written or co-written SAS training courses for advanced statistical methods, including: multivariate statistics, linear and generalized linear mixed models, multilevel models, structural equation models, imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. She also teaches SAS courses using SAS/IML®, SAS® Enterprise Guide®, SAS® Enterprise Miner™, SAS Forecast Studio and JMP® software.


Sharad Saxena
Principal Analytical Training Consultant, SAS

Dr. Sharad Saxena is a Principal Analytical Training Consultant based at the SAS R&D center in Pune, India. Working in the field of statistics and analytics since 2000, he provides education consulting in advanced analytics and machine learning around the globe. Saxena has more than 35 publications, including research papers in journals such as the Journal of Statistical Planning and Inference, Communications in Statistics – Theory and Methods, Statistica, Statistical Papers, and Vikalpa. He is also a co-author of Randomness and Optimal Estimation in Data Sampling.

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