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Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series) - Tapa dura

Libro 2 de 36: Chapman & Hall/CRC Data Science

Kuhn, Max; Johnson, Kjell

 
9781138079229: Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
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ISBN 10: 1138079227 ISBN 13: 9781138079229
Editorial: Chapman and Hall/CRC, 2019

Sinopsis

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Max Kuhn, Ph.D., is a software engineer at RStudio. He worked in 18 years in drug discovery and medical diagnostics applying predictive models to real data. He has authored numerous R packages for predictive modeling and machine learning.

Kjell Johnson, Ph.D., is the owner and founder of Stat Tenacity, a firm that provides statistical and predictive modeling consulting services. He has taught short courses on predictive modeling for the American Society for Quality, American Chemical Society, International Biometric Society, and for many corporations.

Kuhn and Johnson have also authored Applied Predictive Modeling, which is a comprehensive, practical guide to the process of building a predictive model. The text won the 2014 Technometrics Ziegel Prize for Outstanding Book.

De la contraportada

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Chapman and Hall/CRC
Año de publicación
2019
Idioma
Inglés
ISBN 10 
1138079227
ISBN 13 
9781138079229
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
314
Contacto del fabricante
Taylor and Francis, GPSR Taylor and Francis
gpsr@taylorandfrancis.com

Taylor & Francis Verlag GmbH, Kaufingerstraße 24
München
80331
Alemania