Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions

Seni, Giovanni; Elder, John

ISBN 10: 1608452840 / ISBN 13: 9781608452842

Published by Morgan and Claypool Publishers, 2010

About this Item

This book is in good condition, with minimal signs of wear and tear.

Seller Inventory # GWKV.1608452840.G

Bibliographic Details

• Title: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
• Publisher: Morgan and Claypool Publishers
• Publication Date: 2010
• Language: English
• ISBN 10: 1608452840
• ISBN 13: 9781608452842
• Binding: Soft cover
• Condition: good

About this title

Synopsis

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. The practical implementations of ensemble methods are enormous. Most current implementations of them are quite primitive and this book will definitely raise the state of the art. Giovanni Seni's thorough mastery of the cutting-edge research and John Elder's practical experience have combined to make an extremely readable and useful book. - from Foreword 1 by Jaffray Woodriff

Product Description

Ensemble Methods in Data Mining 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 distil...