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Shapley Value in Machine Learning Modeling: Optimizing Decision-Making in Coworking Spaces

Articolo
Data di Pubblicazione:
2024
Citazione:
Shapley Value in Machine Learning Modeling: Optimizing Decision-Making in Coworking Spaces / Ciano, Tiziana; Ferrara, Massimiliano. - In: APPLIED MATHEMATICAL SCIENCES. - ISSN 1314-7552. - 18:9(2024), pp. 419-441. [10.12988/ams.2024.919155]
Abstract:
Game Theory is a mathematical approach to interactive decision-making situations, focusing on players and strategies. The Shapley Value is a fundamental concept in cooperative Game Theory, as it provides a fair method for distributing gains or costs among players. This study calculates the Shapley Value within machine learning models to determine the marginal contribution to the success of collaborative projects, helping to identify investments to maximize the success of coworking spaces in mountain areas. Machine learning models offer valuable insights to predict investments and strategic decisions in a mountain coworking space, ensuring and maximizing its success. The Gradient Boosting model excels at identifying key features such as internet connectivity and accessibility in mountain environments, allowing decision makers to invest in high quality network infrastructure and accessibility improvements for coworking spaces
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Ciano, Tiziana; Ferrara, Massimiliano
Autori di Ateneo:
FERRARA Massimiliano
Link alla scheda completa:
https://iris.unirc.it/handle/20.500.12318/150806
Link al Full Text:
https://iris.unirc.it//retrieve/handle/20.500.12318/150806/402582/Ferrara_2024_AMS_Shapley%20Value_editor.pdf
Pubblicato in:
APPLIED MATHEMATICAL SCIENCES
Journal
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https://www.m-hikari.com/ams/ams-2024/ams-9-12-2024/919155.html
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