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Feature engineering and selection : a practical approach for predictive models / Max Kuhn, Kjell Johnson.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC data science seriesPublication details: Boca Raton : CRC Press, Taylor & Francis Group, c2020.Description: xv, 297 p. : illISBN:
  • 1138079227
  • 9781138079229
Subject(s): LOC classification:
  • TJ217.6 .K84 2020
Contents:
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.
Summary: 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.
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Books Books Punsarn Library General Stacks TJ217.6 .K84 2020 (Browse shelf(Opens below)) Available PNLIB21060074
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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.

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