Tree-Based Machine Learning Methods in SAS® Viya®
By Sharad Saxena
Anticipated publication date: Fourth quarter 2021
Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This book covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya.
Operations Research for Social Good: A Practitioner's Introduction to Operations Research through Applications for Social Good with Python and SAS
By Natalia Summerville and Rob Pratt
Anticipated publication date: First quarter 2022
Latest technological advances allow data analytics practitioners to solve large problems better and faster with state-of-art Artificial Intelligence (AI) tools. At the same time, humanity faces overarching challenges such as climate crisis, children malnutrition, systemic racism, global pandemics, among others. Fortunately, technological advances in AI can support solution development for current world challenges. This book’s purpose is to expand Operations Research (OR) applications for social good by applying OR methodologies typically required in engineering curricula to applications targeted to make this world a better place. This book also provides skills to model and solve OR problems with both SAS and Python as well as practical tools and tips to bridge the gap between academic learning and real-world implementations.
Building Regression Models with SAS®: An Introduction for Data Scientists
By Robert N. Rodriguez
Anticipated publication date: Second quarter 2022
This book will provide readers with a high-level awareness and understanding of newer regression modeling procedures in SAS that are valuable for supervised machine learning, predictive analytics, and statistical modeling. This book will explain the relative benefits of these procedures, introduce the procedures with basic examples, and help users navigate to procedures and methods that meet their needs. The audience for this book includes newcomers to SAS—in particular, data scientists—who might have encountered some of the methods in open source software and are unaware of what SAS offers. The audience also includes longtime SAS users who are familiar with the REG procedure but have not kept up with the availability of newer and more effective procedures in SAS/STAT and SAS Viya.
Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning
By Terisa Roberts and Stephen Tonna
Anticipated publication date: Third quarter 2022
This book introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing artificial intelligence into the risk management process.