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Statistical Learning from a Regression Perspective (Springer Texts- VERY GOOD)
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Specifiche dell'oggetto
- Condizione
- Brand
- Unbranded
- Book Title
- Statistical Learning from a Regression Perspective (Springer Tex
- MPN
- Does not apply
- ISBN
- 9783319440477
Informazioni su questo prodotto
Product Identifiers
Publisher
Springer
ISBN-10
3319440470
ISBN-13
9783319440477
eBay Product ID (ePID)
229154903
Product Key Features
Number of Pages
Xxv, 347 Pages
Publication Name
Statistical Learning from a Regression Perspective
Language
English
Publication Year
2016
Subject
Public Health, Probability & Statistics / General, Probability & Statistics / Regression Analysis, Statistics
Type
Textbook
Subject Area
Mathematics, Social Science, Medical
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Weight
31.3 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Edition Number
2
Intended Audience
Trade
Dewey Edition
22
Reviews
"The intended audience includes advanced undergraduate and graduate students biostatistics in the fields of social science and life science, as well as researchers who want to apply statistical learning procedures to scientific and policy problems. ... This is an excellent overview of statistical learning applications. It is strongly recommended to advanced researchers and statisticians particularly interested in the social and behavioral aspects of data analysis." (Puja Sitwala, Doody's Book Reviews, January, 2017)
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.536
Table Of Content
Statistical Learning as a Regression Problem.- Splines, Smoothers, and Kernels.- Classification and Regression Trees (CART).- Bagging.- Random Forests.- Boosting.- Support Vector Machines.- Some Other Procedures Briefly.- Broader Implications and a Bit of Craft Lore.
Synopsis
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., 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., This book 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., 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. "
LC Classification Number
QA276-280
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Go.Retro.Go.store
100% di Feedback positivi•12 mila oggetti venduti
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Feedback sul venditore (4.356)
- f***a (2089)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoVery positive experience! Excellent seller communication! I reached out with some questions and the seller got back to me immediately, and then shipped the next day after I paid for everything. Packed very carefully and securely to prevent damage! Both hoodies arrived as described, only 2 days after ordering (all the way across the country). Seller kindly combined shipping cost and shipped both hoodies together, saving me a lot in shipping cost. Good prices. Nice shirts. Thank you very much
- k***k (360)- Feedback lasciato dall'acquirente.Mese scorsoAcquisto verificatoThis seller was very good with communication. The packaging of the slipcase and books was excellent as item arrived safely. Condition of books was as described. I don't think the books have been opened, let alone read. Value was great for what I received and the market for the books. Thanks for this perfect experience!
- e***g (69)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoThe shipping was very fast. The pants were well packed and protected in a sealed inner bag. The description and photos of the product were accurate. The pants are even nicer than I had expected and value for what I paid exceeded my expectations. I’m very pleased with this small family company and I will buy items from them in the future! They are on my saved list.