Michel Denuit,Donatien Hainaut,Julien Trufin
Effective Statistical Learning Methods for Actuaries II: Tree-Based Methods and Extensions
Effective Statistical Learning Methods for Actuaries II: Tree-Based Methods and Extensions
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This book summarizes the state-of-the-art in tree-based methods for insurance, including regression trees, random forests, and boosting methods, and demonstrates how to assess their predictive performance. It is written for actuaries and covers insurance data analytics with applications to P&C, life, and health insurance.
Format: Paperback / softback
Length: 228 pages
Publication date: 17 November 2020
Publisher: Springer Nature Switzerland AG
This comprehensive book delves into the cutting-edge realm of tree-based methods in insurance, encompassing regression trees, random forests, and boosting techniques. It serves as a valuable resource for actuaries seeking to harness the power of big data and transform it into valuable opportunities. The book offers a balanced approach, alternating between methodological discussions and numerical illustrations or case studies, all executed using the R statistical software. The technical prerequisites are carefully designed to ensure accessibility to a broad audience, particularly for master's students in actuarial sciences and actuaries seeking to enhance their machine learning skills.
As the second installment in a three-volume series, Effective Statistical Learning Methods for Actuaries, this book provides a comprehensive overview of insurance data analytics, with applications spanning P&C, life, and health insurance. It covers a wide range of topics, including model selection, feature engineering, performance evaluation, and practical considerations. The authors, who are experienced actuaries themselves, have tailored the content to meet the specific needs of the actuarial profession, making it an invaluable resource for practitioners seeking to stay ahead in the ever-evolving field of data analytics.
With its extensive coverage, practical examples, and user-friendly approach, this book is an essential tool for actuaries, insurance professionals, and researchers interested in leveraging advanced statistical learning methods to improve insurance underwriting, risk management, and customer engagement. Whether you are a novice or an experienced practitioner, this book will provide you with the knowledge and skills necessary to unlock the full potential of insurance data and drive innovation in the industry.
Weight: 372g
Dimension: 155 x 233 x 18 (mm)
ISBN-13: 9783030575557
Edition number: 1st ed. 2020
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