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.

Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered – step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills. In this book, you will learn about: Supervised and unsupervised machine learning Data preparation and dealing with missing and unstructured data Model building and selection Improving and optimizing models Model deployment and monitoring performance

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

"Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you."--Back cover.

Learn how to access analytics from SAS Cloud Analytic Services (CAS) using R and the SAS Viya platform. SAS Viya : The R Perspective is a general-purpose introduction to using R with the SAS Viya platform. SAS Viya is a high-performance, fault-tolerant analytics architecture that can be deployed on both public and private cloud infrastructures. This book introduces an entirely new way of using SAS statistics from R, taking users step-by-step from installation and fundamentals to data exploration and modeling. SAS Viya is made up of multiple components. The central piece of this ecosystem is SAS Cloud Analytic Services (CAS). CAS is the cloud-based server that all clients communicate with to run analytical methods. While SAS Viya can be used by various SAS applications, it also enables you to access analytic methods from SAS, R, Python, Lua, and Java, as well as through a REST interface using HTTP or HTTPS. The R client is used to drive the CAS component directly using commands and actions that are familiar to R programmers. Key features of this book include: Connecting to CAS from R Loading, managing, and exploring CAS Data from R Executing CAS actions and processing the results Handling CAS action errors Modeling continuous and categorical data This book is intended for R users who want to access SAS analytics as well as SAS users who are interested in trying R. Familiarity with R would be helpful before using this book although knowledge of CAS is not required. However, you will need to have a CAS server set up and running to execute the examples in this book.

Gain Powerful Insights with SAS Viya! Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

Machine Learning for Business Analytics Machine learning—also known as data mining or data analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.