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Effective statistical learning metho...

Denuit, Michel.

Effective statistical learning methods for actuaries.II,Tree-based methods and extensions

Record Type:	Electronic resources : Monograph/item
Title/Author:	Effective statistical learning methods for actuaries.by Michel Denuit, Donatien Hainaut, Julien Trufin.
remainder title:	Tree-based methods and extensions
Author:	Denuit, Michel.
other author:	Hainaut, Donatien.
Published:	Cham :Springer International Publishing :2020.
Description:	x, 228 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Regression analysis.
Online resource:	https://doi.org/10.1007/978-3-030-57556-4
ISBN:	9783030575564$q(electronic bk.)

Effective statistical learning methods for actuaries.II,Tree-based methods and extensions
Denuit, Michel.

Effective statistical learning methods for actuaries.II,Tree-based methods and extensions[electronic resource] /Tree-based methods and extensionsby Michel Denuit, Donatien Hainaut, Julien Trufin. - Cham :Springer International Publishing :2020. - x, 228 p. :ill., digital ;24 cm. - Springer actuarial lecture notes,2523-3289. - Springer actuarial lecture notes..

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 health insurance.

ISBN: 9783030575564$q(electronic bk.)

Standard No.: 10.1007/978-3-030-57556-4doiSubjects--Topical Terms:

181872
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Effective statistical learning methods for actuaries.II,Tree-based methods and extensions
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520 $a 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.
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