Da: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
EUR 11,10
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Aggiungi al carrelloCondizione: New. Second edition, 347 pp., hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
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Aggiungi al carrelloCondizione: New. pp. 384.
EUR 38,25
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Aggiungi al carrelloCondizione: New. pp. 384 Illus.
EUR 39,78
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Aggiungi al carrelloCondizione: New. pp. 384.
Editore: Springer-Verlag New York Inc, 2008
ISBN 10: 0387775005 ISBN 13: 9780387775005
Lingua: Inglese
Da: URW Books Store, CASPER, WY, U.S.A.
EUR 55,84
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Aggiungi al carrelloHardcover. Condizione: Brand New. Brand New! Fast Delivery, Delivery within 8-14 working Day Only, Excellent Quality, Printing in English Language, Quick delivery by FEDEX & DHL. USPS & UPS Act. Our courier service is not available at PO BOX& APO BOX. Ship from India & United States.
EUR 71,48
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Aggiungi al carrelloCondizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
EUR 74,75
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Aggiungi al carrelloCondizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Da: Basi6 International, Irving, TX, U.S.A.
EUR 74,75
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Aggiungi al carrelloCondizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
EUR 68,58
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 67,48
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EUR 68,15
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EUR 71,50
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EUR 66,79
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 73,75
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EUR 61,68
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Editore: Springer International Publishing, Springer International Publishing Jun 2018, 2018
ISBN 10: 3319829696 ISBN 13: 9783319829692
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 71,68
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 376 pp. Englisch.
Da: Greenway, Chattanooga, TN, U.S.A.
EUR 21,73
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Aggiungi al carrelloHardcover. Condizione: Very good condition. very clean,fast ship.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 71,22
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Editore: Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 3319829696 ISBN 13: 9783319829692
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 71,68
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided.
Da: BooksRun, Philadelphia, PA, U.S.A.
EUR 23,58
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Aggiungi al carrelloHardcover. Condizione: Very Good. 2008. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Da: Chiron Media, Wallingford, Regno Unito
EUR 70,35
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Aggiungi al carrelloPaperback. Condizione: New.
EUR 79,27
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 80,45
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Editore: Springer International Publishing, Springer Nature Switzerland, 2021
ISBN 10: 3030429237 ISBN 13: 9783030429232
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 90,94
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications.The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture.The third edition considers significant advances in recent years, among which are:the development of overarching, conceptual frameworks for statistical learning;the impact of 'big data' on statistical learning;the nature and consequences of post-model selection statistical inference;deep learning in various forms;the special challenges to statistical inference posed by statistical learning;the fundamental connections between data collection and data analysis;interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy.This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research.Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
Da: California Books, Miami, FL, U.S.A.
EUR 107,64
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Editore: Springer New York, Springer US Jul 2008, 2008
ISBN 10: 0387775005 ISBN 13: 9780387775005
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical.Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one¿s data and not apply statistical learning procedures that require more than the data can provide.The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 384 pp. Englisch.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 117,60
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 117,60
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Aggiungi al carrelloCondizione: New. In English.
Editore: Springer New York, Springer US, 2008
ISBN 10: 0387775005 ISBN 13: 9780387775005
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 111,53
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one's data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 76,60
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