• ISBN13: 9781119421092
• Binding: Hardcover
• Subject: Economics
• Publisher: Wiley
• Publisher Imprint: Wiley-Blac
• Publication Date: April 16, 2019
• Pages: 224
• Original Price: USD 139.95
• Language: English
• Edition: N/A
• Item Weight: 499 grams

About the Book An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning</b> Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods' basic characteristics and explain the types of problems that can emerge in its application. Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: <ul> <li>Offers an important text that has been tested both in the classroom and at tutorials at conferences</li> <li>Contains authoritative information written by leading experts in the field</li> <li>Presents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence </li> <li>Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification trees</li> </ul> Written for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.