| 书目名称 | Effective Statistical Learning Methods for Actuaries II |
| 副标题 | Tree-Based Methods a |
| 编辑 | Michel Denuit,Donatien Hainaut,Julien Trufin |
| 视频video | |
| 概述 | Provides an exhaustive and self-contained presentation of tree-based methods applied to insurance.Gives a rigorous statistical analysis of tree-based methods.Fills a gap in the literature on artificia |
| 丛书名称 | Springer Actuarial |
| 图书封面 |  |
| 描述 | .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 insurancedata analytics with applications to P&C, life and health insurance.. |
| 出版日期 | Textbook 2020 |
| 关键词 | tree-based methods for insurance; supervised learning; machine learning; actuarial modeling; insurance r |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-57556-4 |
| isbn_softcover | 978-3-030-57555-7 |
| isbn_ebook | 978-3-030-57556-4Series ISSN 2523-3262 Series E-ISSN 2523-3270 |
| issn_series | 2523-3262 |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 18:00:22
