SHAP for Actuaries : Explain any Model

This tutorial gives an overview of SHAP (SHapley Additive exPlanation), one of the most commonly used techniques for examining a black-box machine learning (ML) model. Besides providing the necessary game theoretic background, we show how typical SHAP analyses are performed and used to gain insights about the model. The methods are illustrated on a simulated insurance data set of car claim frequencies using different ML models and different SHAP algorithms

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Year of publication:	[2023]
Authors:	Mayer, Michael ; Meier, Daniel ; Wuthrich, Mario V.
Publisher:	[S.l.] : SSRN
Subject:	Theorie \| Theory \| Versicherungsmathematik \| Actuarial mathematics

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Extent:	1 Online-Ressource (25 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 15, 2023 erstellt
Other identifiers:	10.2139/ssrn.4389797 [DOI]
Classification:	G22 - Insurance; Insurance Companies ; C45 - Neural Networks and Related Topics ; c18 ; C52 - Model Evaluation and Testing ; c55 ; C71 - Cooperative Games
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014362422