01805cam a2200301Ii 4500 on1099869989 OCoLC ta 210114s2020 flua b 001 0 eng d 1138079227 9781138079229 (OCoLC)1099869989 TJ217.6 .K84 2020 Kuhn, Max. Feature engineering and selection : a practical approach for predictive models / Max Kuhn, Kjell Johnson. Boca Raton : CRC Press, Taylor & Francis Group, c2020. xv, 297 p. : ill. Chapman & Hall/CRC data science series Includes bibliographical references and index. 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. 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. Predictive control Data processing. Predictive control Mathematical models. R (Computer program language) Johnson, Kjell. Chapman & Hall/CRC data science series. lcc BK 0 0 0 0 PNLIB PNLIB GEN 2021-06-17 TJ217.6 .K84 2020 PNLIB21060074 2021-06-17 2021-06-17 BK 260 260