Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Paperback)

Giovanni Seni, John Elder

  • 出版商: Morgan & Claypool
  • 出版日期: 2010-02-24
  • 售價: $1,430
  • 貴賓價: 9.5$1,359
  • 語言: 英文
  • 頁數: 126
  • 裝訂: Paperback
  • ISBN: 1608452840
  • ISBN-13: 9781608452842
  • 相關分類: Data-mining
  • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

集成方法被稱為過去十年中數據挖掘和機器學習中最具影響力的發展。它們將多個模型結合成一個通常比其組成部分中最好的模型更準確的模型。集成方法可以為工業挑戰提供關鍵的提升,從投資時機到藥物發現,從詐騙檢測到推薦系統,其中預測準確性比模型可解釋性更重要。

集成方法適用於所有建模算法,但本書專注於決策樹,以最清晰的方式解釋它們。在描述樹及其優點和缺點之後,作者概述了正則化的概念,現在被認為是現代集成算法優越性能的關鍵原因。本書繼續介紹了兩個最近的發展:重要性抽樣(IS)和規則集成(RE)。IS揭示了經典的集成方法(bagging、隨機森林和提升)是單一算法的特殊情況,從而展示了如何提高它們的準確性和速度。RE是從決策樹集成中衍生出的線性規則模型。它們是最可解釋的集成版本,對於信用評分和故障診斷等應用至關重要。最後,作者解釋了集成方法如何在新數據上實現更高的準確性,儘管它們(表面上)更為複雜的悖論。

本書針對初學者和高級分析研究人員和從業人員,特別是工程、統計和計算機科學領域的人士。對於對集成方法接觸較少的人,他們將了解為什麼以及如何應用這一突破性方法,而高級從業人員將獲得建立更強大模型的見解。全書提供了R語言的代碼片段,以說明所描述的算法並鼓勵讀者嘗試這些技術。