Lingua: Inglese
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608452840 ISBN 13: 9781608452842
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Lingua: Inglese
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608452840 ISBN 13: 9781608452842
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Lingua: Inglese
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608452840 ISBN 13: 9781608452842
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Lingua: Inglese
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608452840 ISBN 13: 9781608452842
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Lingua: Inglese
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608452840 ISBN 13: 9781608452842
Da: Bookbot, Prague, Repubblica Ceca
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Aggiungi al carrelloSoftcover. Condizione: Fine. Leichte Risse. "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.
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Aggiungi al carrelloCondizione: New.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: Springer International Publishing, 2010
ISBN 10: 3031007719 ISBN 13: 9783031007712
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Giovanni Seni is a Senior Scientist with Elder Research, Inc. and directs ERI s Western office. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer .
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Ensemble Methods in Data Mining | Improving Accuracy Through Combining Predictions | Giovanni Seni (u. a.) | Taschenbuch | xvi | Englisch | 2010 | Springer | EAN 9783031007712 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.