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

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

Authors
Publisher Springer Nature Switzerland AG
Year 2020
Pages 228
Version paperback
Readership level Professional and scholarly
Language English
ISBN 9783030575557
Categories Insurance & actuarial studies
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265.65 PLN / €56.96 / £49.44
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Book description

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, master's 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.

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

Table of contents

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.

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