Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.
This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.
The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
Table of Contents
Predictive Learning and Decision Trees
Model Complexity, Model Selection and Regularization
Importance Sampling and the Classic Ensemble Methods
Rule Ensembles and Interpretation Statistics
About the Author(s)Giovanni Seni
, Elder Research, Inc. and Santa Clara University
Giovanni Seni is a Senior Scientist with Elder Research, Inc. and directs ERI's Western office. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. Giovanni is an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class. He received a B.S. in Computer Engineering from Universidad de Los Andes (Bogota, Colombia) in 1989, and a Ph.D. in Computer Science from State University of New York at Buffalo (SUNY Buffalo) in 1995, where he studied on a Fulbright scholarship. He also holds a certificate in Data Mining and Applications from the Department of Statistics at Stanford University.John Elder
, Elder Research, Inc.
Dr. John F. Elder IV heads a data mining consulting team with offices in Charlottesville, Virginia, Washington DC, and Mountain View, California (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on federal, commercial, investment, and security applications of advanced analytics, including text mining, stock selection, image recognition, biometrics, process optimization, cross-selling, drug efficacy, credit scoring, risk management, and fraud detection. ERI has become the largest and most experienced data mining consultancy. John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 15 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice's Computational & Applied Mathematics department. Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and was co-chair of the 2009 Knowledge Discovery and Data Mining conference, in Paris. His courses on analysis techniques - taught at dozens of universities, companies, and government labs - are noted for their clarity and effectiveness. John was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security. His award-winning book for practitioners of analytics, with Bob Nisbet and Gary Miner - The Handbook of Statistical Analysis & Data Mining Applications - was published in May 2009. John is a follower of Christ and the proud father of 5.