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Ensemble methods in data mining : improving accuracy through combining predictions

Author: Giovanni Seni; John F Elder
Publisher: [San Rafael, Calif.] : Morgan & Claypool Publishers, ©2010.
Series: Synthesis lectures on data mining and knowledge discovery, #2.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Seni, Giovanni.
Ensemble methods in data mining.
[San Rafael, Calif.] : Morgan & Claypool Publishers, ©2010
(OCoLC)500783957
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Giovanni Seni; John F Elder
ISBN: 9781608452859 1608452859
OCLC Number: 599854566
Description: 1 online resource (xvi, 108 pages) : illustrations.
Contents: 1. Ensembles discovered --
Building ensembles --
Regularization --
Real-world examples: credit scoring + the Netflix challenge --
Organization of this book.
Series Title: Synthesis lectures on data mining and knowledge discovery, #2.
Responsibility: Giovanni Seni, John F. Elder.

Abstract:

Ensemble combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges where predictive accuracy is more  Read more...

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