Description: Effective Statistical Learning Methods for Actuaries II : Tree-based Methods and Extensions, Paperback by Denuit, Michel; Hainaut, Donatien; Trufin, Julien, ISBN 3030575551, ISBN-13 9783030575557, Like New Used, Free shipping in the US
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 th 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.
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Number of Pages: X, 228 Pages
Language: English
Publication Name: Effective Statistical Learning Methods for Actuaries II : Tree-Based Methods and Extensions
Publisher: Springer International Publishing A&G
Subject: Probability & Statistics / General, Applied, Business Mathematics
Publication Year: 2020
Item Weight: 16 Oz
Type: Textbook
Subject Area: Mathematics, Business & Economics
Item Length: 9.3 in
Author: Michel Denuit, Donatien Hainaut, Julien Trufin
Item Width: 6.1 in
Series: Springer Actuarial Ser.
Format: Trade Paperback
