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Feature Engineering and Selection: A Practical Approach for Predictive Models - Couverture rigide

Livre 2 sur 36: Chapman & Hall/CRC Data Science

Kuhn, Max; Johnson, Kjell

 
9781138079229: Feature Engineering and Selection: A Practical Approach for Predictive Models
Couverture rigide
ISBN 10 : 1138079227 ISBN 13 : 9781138079229
Editeur : CRC Press, 2019

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

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.

À propos de la quatrième de couverture

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
CRC Press
Date d'édition
2019
Langue
anglais
ISBN 10 
1138079227
ISBN 13 
9781138079229
Reliure
Relié
Numéro d'édition
1
Nombre de pages
297
Coordonnées du fabricant
non disponible
Personne responsable
GPSR Kontakt
gpsr@libri.de

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