LEADER 03644nam 22006615 450 001 9910484306203321 005 20251113193346.0 010 $a9783030575564 010 $a303057556X 024 7 $a10.1007/978-3-030-57556-4 035 $a(CKB)4100000011586001 035 $a(DE-He213)978-3-030-57556-4 035 $a(MiAaPQ)EBC6455952 035 $a(PPN)252509145 035 $a(MiAaPQ)EBC31862527 035 $a(Au-PeEL)EBL31862527 035 $a(EXLCZ)994100000011586001 100 $a20201116d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEffective Statistical Learning Methods for Actuaries II $eTree-Based Methods and Extensions /$fby Michel Denuit, Donatien Hainaut, Julien Trufin 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (X, 228 p. 68 illus., 6 illus. in color.) 225 1 $aSpringer Actuarial Lecture Notes,$x2523-3297 311 08$a9783030575557 311 08$a3030575551 327 $aChapter 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. 330 $aThis book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, masters students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. 410 0$aSpringer Actuarial Lecture Notes,$x2523-3297 606 $aActuarial science 606 $aNeural networks (Computer science) 606 $aStatistics 606 $aActuarial Mathematics 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aActuarial science. 615 0$aNeural networks (Computer science). 615 0$aStatistics. 615 14$aActuarial Mathematics. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a519.536 700 $aDenuit$b Michel$0781288 702 $aHainaut$b Donatien 702 $aTrufin$b Julien 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484306203321 996 $aEffective Statistical Learning Methods for Actuaries II$94464288 997 $aUNINA