Quotation Fissler, Tobias, Lorentzen, Christian, Mayer, Michael. 2022. Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice.


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Abstract

One of the main tasks of actuaries and data scientists is to build good predictive models for certain phenomena such as the claim size or the number of claims in insurance. These models ideally exploit given feature information to enhance the accuracy of prediction. This user guide revisits and clarifies statistical techniques to assess the calibration or adequacy of a model on the one hand, and to compare and rank different models on the other hand. In doing so, it emphasises the importance of specifying the prediction target at hand a priori and of choosing the scoring function in model comparison in line with this target. Guidance for the practical choice of the scoring function is provided. Striving to bridge the gap between science and daily practice in application, it focuses mainly on the pedagogical presentation of existing results and of best practice. The results are accompanied and illustrated by two real data case studies on workers' compensation and customer churn.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice
Year 2022
URL https://doi.org/10.48550/arXiv.2202.12780

Associations

People
Fissler, Tobias (Details)
External
Lorentzen, Christian (Mobiliar, Switzerland)
Mayer, Michael ((Mobiliar, Switzerland), Switzerland)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1117 Actuarial mathematics (Details)
1162 Statistics (Details)
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