Effective Statistical Learning Methods for Actuaries II - Tree-Based Methods and Extensions (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Actuarial
Release date:	November 2020
First published:	2020
Authors:	Michel Denuit • Donatien Hainaut • Julien Trufin
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Paperback
Pages:	228
Edition:	1st ed. 2020
ISBN-13:	978-3-03-057555-7
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Business & Economics > Finance & accounting > Finance > Insurance > General Books > Money & Finance > Insurance > General Promotions
LSN:	3-03-057555-1
Barcode:	9783030575557

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