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Google圖書搜尋
Ensemble Methods in Data Mining
Giovanni Seni
John Fletcher Elder
其他書名
Improving Accuracy Through Combining Predictions
出版
Morgan & Claypool Publishers
, 2010
主題
Computers / Artificial Intelligence / General
Computers / Data Science / Data Analytics
Mathematics / Set Theory
ISBN
1608452840
9781608452842
URL
http://books.google.com.hk/books?id=CdyZpAWt6YcC&hl=&source=gbs_api
EBook
SAMPLE
註釋
"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."--Publisher's website.