Effective Statistical Learning Methods for Actuaries II Tree-Based Methods and Extensions

This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn th...

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Bibliographic Details
Main Authors: Denuit, Michel, Hainaut, Donatien (Author), Trufin, Julien (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2020, 2020
Edition:1st ed. 2020
Series:Springer Actuarial Lecture Notes
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary:This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, masters students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance
Physical Description:X, 228 p. 68 illus., 6 illus. in color online resource
ISBN:9783030575564