Feature Engineering and Selection: A Practical Approach for Predictive Models

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CRC Press, 25.07.2019 - 310 Seiten
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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.

 

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Inhalt

Preface
Predicting Risk of Ischemic Stroke
A Review of the Predictive Modeling Process
Exploratory Visualizations
Encoding Categorical Predictors
Engineering Numeric Predictors
Detecting Interaction Effects
Handling Missing Data
Working with Profile Data
Feature Selection Overview
Greedy Search Methods
Global Search Methods
Bibliography
Index
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Über den Autor (2019)

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.

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