Effective Statistical Learning Methods for Actuaries II : Tree-Based Methods and Extensions. 1st ed. 2020

種類:

電子ブック

責任表示:

by Michel Denuit, Donatien Hainaut, Julien Trufin

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Denuit, Michel.
• Hainaut, Donatien.
• Trufin, Julien.
• SpringerLink (Online service)

シリーズ名:

Springer Actuarial Lecture Notes ;

ISBN:

9783030575564 [303057556X]    

注記:

Chapter 1: Introductio -- Chapter 2 : Performance Evaluation -- Chapter 3 Regression Trees -- Chapter 4 Bagging Trees and Random Forests -- Chapter 5 Boosting Trees -- Chapter 6 Other Measures for Model Comparison.
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

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