Tree-Based Machine Learning Methods in SAS Viya

Discover how to build decision trees using SAS Viya!

Tree-Based Machine Learning Methods in SAS Viya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and gradient boosted trees. Each chapter introduces a new data concern and then walks you through tweaking the modeling approach, modifying the properties, and changing the hyperparameters, thus building an effective tree-based machine learning model. Along the way, you will gain experience making decision trees, forests, and gradient boosted trees that work for you.

By the end of this book, you will know how to:

• build tree-structured models, including classification trees and regression trees.
• build tree-based ensemble models, including forest and gradient boosting.
• run isolation forest and Poisson and Tweedy gradient boosted regression tree models.
• implement open source in SAS and SAS in open source.
• use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.

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 the area of advanced analytics and machine learning across the globe including the UK, USA, Singapore, Italy, Australia, Netherlands, Middle East, China, Philippines, Nigeria, Hong Kong, Malaysia, Indonesia, Mexico, and India for a variety of SAS customers in banking, insurance, retail, government, health, agriculture, and telecommunications. Dr. Saxena earned a bachelor's degree in mathematics with statistics and economics minors, a master's degree in statistics, and a Ph.D. in statistics from the School of Studies in Statistics at Vikram University, India. Dr. 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 the book, Randomness and Optimal Estimation in Data Sampling. Overall, Dr. Saxena has more than two decades of rich experience in research, teaching, training, consulting, writing, and education product design, more than 14 years of which have been with SAS and the remaining in academia as a faculty member with some top-notch institutes in India like the Institute of Management Technology, Ghaziabad; Institute of Management, Nirma University, and more.