04366nam a22006495i 4500 000717964 DE-He213 20240923053748.0 20230810160757.0 cr nn 008mamaa 130517s2013 xxu| s |||| 0|eng d NEZPRACOVANÝ IMPORT 9781461468493 978-1-4614-6849-3 10.1007/978-1-4614-6849-3 doi QH323.5 PBT bicssc MED090000 bisacsh PBT thema 570.15195 23 Kuhn, Max. author. aut http://id.loc.gov/vocabulary/relators/aut Applied Predictive Modeling [elektronický zdroj] / by Max Kuhn, Kjell Johnson. 1st ed. 2013. New York, NY : Springer New York : Imprint: Springer, 2013. XIII, 600 p. 310 illus., 296 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda General Strategies -- Regression Models -- Classification Models -- Other Considerations -- Appendix -- References -- Indices. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance—all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process elektronické knihy fd186907 czenas electronic books eczenas Biometry. Mathematical statistics Data processing. Statistics . Biostatistics. Statistics and Computing. Statistics. Johnson, Kjell. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) Springer Nature eBook Printed edition: 9781461468486 Printed edition: 9781461468509 Printed edition: 9781493979363 Printed edition: 9781071630914 https://zdroje.vse.cz/login?url=https://doi.org/10.1007/978-1-4614-6849-3 Plný text SPRINGER-OWNED EB PhDinfra PAR 000301310 UEP01 776 E-book Tištěná verze NEPOSILAT EB ZDB-2-SMA ZDB-2-SXMS Mathematics and Statistics (SpringerNature-11649) Mathematics and Statistics (R0) (SpringerNature-43713)