國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Effective Statistical Learning Methods for Actuaries II/ by Michel Denuit, Donatien Hainaut, Julien Trufin.
Reminder of title:	Tree-Based Methods and Extensions /
Author:	Denuit, Michel.
other author:	Hainaut, Donatien.
Description:	X, 228 p. 68 illus., 6 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Actuarial science. -
Online resource:	https://doi.org/10.1007/978-3-030-57556-4
ISBN:	9783030575564

Effective Statistical Learning Methods for Actuaries II = Tree-Based Methods and Extensions /
Denuit, Michel.

Effective Statistical Learning Methods for Actuaries IITree-Based Methods and Extensions /[electronic resource] :by Michel Denuit, Donatien Hainaut, Julien Trufin. - 1st ed. 2020. - X, 228 p. 68 illus., 6 illus. in color.online resource. - 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

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

943795
Actuarial science.

LC Class. No.: HG8779-8793

Dewey Class. No.: 368.01

Effective Statistical Learning Methods for Actuaries II = Tree-Based Methods and Extensions /
LDR:02702nam a22003975i 4500 001 1026366
003 DE-He213
005 20201116115338.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030575564 $9 978-3-030-57556-4
024 7 $a 10.1007/978-3-030-57556-4 $2 doi
035 $a 978-3-030-57556-4
050 4 $a HG8779-8793
072 7 $a KFFN $2 bicssc
072 7 $a BUS033000 $2 bisacsh
072 7 $a KFFN $2 thema
082 0 4 $a 368.01 $2 23
100 1 $a Denuit, Michel. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300484
245 1 0 $a Effective Statistical Learning Methods for Actuaries II $h [electronic resource] : $b Tree-Based Methods and Extensions / $c by Michel Denuit, Donatien Hainaut, Julien Trufin.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a X, 228 p. 68 illus., 6 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Actuarial Lecture Notes, $x 2523-3289
505 0 $a 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.
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.
650 0 $a Actuarial science. $3 943795
650 0 $a Neural networks (Computer science) . $3 1253765
650 0 $a Statistics . $3 1253516
650 1 4 $a Actuarial Sciences. $3 884190
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
700 1 $a Hainaut, Donatien. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300485
700 1 $a Trufin, Julien. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300486
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030575557
776 0 8 $i Printed edition: $z 9783030575571
830 0 $a Springer Actuarial Lecture Notes, $x 2523-3289 $3 1300487
856 4 0 $u https://doi.org/10.1007/978-3-030-57556-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)