Effective Statistical Learning Methods for Actuaries II: Tree-Based Methods and Extensions - Donatien Hainaut,Michel Denuit,Julien Trufin
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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 ... Full description
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Description
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.
More Information
| Author | Donatien Hainaut, Michel Denuit, Julien Trufin |
|---|---|
| Publisher | Springer Nature Switzerland |
| Release year | 2020 |
| Cover type | Softcover |
| EAN | 9783030575557 |