Max Kuhn, Kjell Johnson

Feature Engineering and Selection (eBook, PDF)

A Practical Approach for Predictive Models

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 310
• Erscheinungstermin: 25. Juli 2019
• Englisch
• ISBN-13: 9781351609470
• Artikelnr.: 57268564

Autorenporträt

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.

Inhaltsangabe

1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic
Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory
Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric
Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9.
Working with Profile Data. 10. Feature Selection Overview. 11. Greedy
Search Methods. 12. Global Search Methods.