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Applied Predictive Modeling 2013th Edition by Max Kuhn, ISBN-13: 978-1461468486
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"Applied Predictive Modeling" by Max Kuhn is a comprehensive guide that focuses on the practical aspects of predictive modeling using statistical techniques and machine learning methods. The book addresses the entire modeling process, from data preprocessing and feature selection to model assessment and deployment. It incorporates various predictive algorithms, including linear regression, decision trees, and ensemble methods, while emphasizing the importance of effective data handling and validation techniques. Kuhn provides real-world examples and case studies using the R programming language, making it accessible for practitioners seeking to enhance their predictive modeling skills in a variety of applications.
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Title: Applied Predictive Modeling
Author: Max Kuhn
Edition: 2013th Edition
ISBN-13: 978-1461468486
Publisher: Springer
Publication Year: 2013
Pages: 480
Language: English
Series: Use R!
Focus: The book covers techniques and applications of predictive modeling, with emphasis on practical implementation in R.
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"Applied Predictive Modeling" by Max Kuhn serves as a comprehensive guide for practitioners looking to master predictive analytics techniques. It effectively bridges theoretical concepts with practical applications, using clear examples and real-world datasets. The book emphasizes the importance of model validation and offers insights into various modeling techniques, including decision trees, ensemble methods, and regression. Kuhn’s emphasis on the R programming language allows readers to apply learned techniques in a familiar environment. Overall, this edition stands as an essential resource for both newcomers and seasoned data scientists aiming to enhance their predictive modeling skills.
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