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201208 ||| eng |
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|a 9783030575564
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| 100 |
1 |
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|a Denuit, Michel
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| 245 |
0 |
0 |
|a Effective Statistical Learning Methods for Actuaries II
|h Elektronische Ressource
|b Tree-Based Methods and Extensions
|c by Michel Denuit, Donatien Hainaut, Julien Trufin
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| 250 |
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|a 1st ed. 2020
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2020, 2020
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| 300 |
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|a X, 228 p. 68 illus., 6 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Statistics
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| 653 |
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|a Actuarial science
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| 653 |
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|a Mathematical Models of Cognitive Processes and Neural Networks
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| 653 |
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|a Neural networks (Computer science)
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| 653 |
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|a Statistics in Business, Management, Economics, Finance, Insurance
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Actuarial Mathematics
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| 700 |
1 |
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|a Hainaut, Donatien
|e [author]
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| 700 |
1 |
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|a Trufin, Julien
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Springer Actuarial Lecture Notes
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| 028 |
5 |
0 |
|a 10.1007/978-3-030-57556-4
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-57556-4?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 368.01
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| 520 |
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|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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