Feature engineering and selection : a practical approach for predictive models - Kuhn, Max.; Johnson, Kjell (Boca Raton : CRC Press, Taylor & Francis Group, 2020)
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 finding 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.
Illustrative example: 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.
Includes bibliographical references and index.
Keywords: Predictive control -- Data processing; Predictive control -- Mathematical models; R (Computer program language)