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机器学习中的组合模型 评分

解释了组合模型的历史,以及原理,并且附上作者自己的研究案例
Synthesis Lectures on Data Mining and Knowledge Discovery Editor Robert grossman, University of Illinois, chicago Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions Giovanni Seni and John F. Elder 2010 Modeling and Data Mining in Blogosphere Nitin Agarwal and Huan Liu 2009 shto 2010 by morgan claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any mcans clcctronic, mcchanical, photocopy, rccording, or any other cxcept for bricf quotations in printed reviews, without the prior permission of the publisher. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions John F. Elder nclaypool.com ISBN: 9781608452842 paperback ISBN:9781608452859 ebook DO110.2200/S00240EL1V01Y200912DMK002 A Publication in the Morgan Claypool Publish hers series SYNTHESIS LECTURES ON DATA MINING AND KNOWLEDGE DISCOVERY Lecture #2 Scrics Editor: Robert Grossman, University of Illinois, Chicago Series issn Synthesis Lectures on Data Mining and Knowledge Discovery Print 2151-0067 Electronic 2151-0075 Ensemble methods in Data mining Improving Accuracy Through Combining Predictions Giovanni seni Elder research, Inc and Santa Clara Universit John F. Elder Elder Research, Inc and University of virginia SYNTHESIS LECTURES ONDATA MINING AND KNOWLEDGE DISCOVERY 2 M MORGAN &CLAYPOOL PUBLISHERS ABSTRACT 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 per formance 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 appli cations 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 insightinto 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 Fried- man)to bring the benefits of ensembles to practitioners The authors would appreciate hearing of errors in or suggested improvements to this book, and may be emailed at seni@datamininglab com and elder ddatamininglab com Errata and updateswillbeavailablefromwww.morganclaypool,com KEYWORDS ensemble methods, rule ensembles, importance sampling, boosting, random forest, bag- ging, regularization, decision trees, data mining, machine learning, pattern recognition model interpretation, model complexity, generalized degrees of freedom iR is an Open Source Language and environment for data analysis and statistical modeling available through the Comprehensive RArchiveNetwork(cran).TheRsystemslibrarypackagesofferextensivefunctionalityandbedownloadedformhttp:// cran. r-project. org/ for many computing platforms. The CRaN web site also has pointers to tutorial and comprehensive documentation. A variety of excellent introductory books are also available; we particularly like Introductory Statistics with R by Peter Dalgaard and Modern Applied Statistics with S by W.N. Venables and B D. Ripley. To the loving memory of our fathers, Tito and Fletcher Contents A cknowieagments.......,....... ,.,,,,,,,,,,,,,,,。,,.,,,,,,,,..X111 Foreword by Jaffray Woodriff F reword by I in kam H ,,.,,XV11 Ensembles discovered 1.1 Building Ense embles 4 1.2 Regularization 6 1.3 Real-World Examples: Credit Scoring the Netflix Challenge 1.4 Organization of This book 2 Predictive Learning and Decision Trees .11 2.1 Decision Trcc Induction Overview 2.2 Decision Tree Properties 18 2.3 Decision Tree limitations 19 3 Model Complexity, Model Selection and Regularization.…………………21 3. 1 What is the"Right" Size of a Tre 21 3.2 Bias-Variance Decomposition ................... 3.3 Regularization 3. 3. 1 Regularization and Cost-Complexity Tree Pruning 25 3. 3. 2 Cross-Validation 26 3.3.3 Regularization via Shrinkage 28 3.3.4 Regularization via Incremental Model Building 32 xample 34 3.3.6 Regularization Summary 37

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