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

Academic Article
Publication Date:
2024
Short description:
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
Iris type:
1.1 Articolo in rivista
List of contributors:
Ciano, Tiziana; Ferrara, Massimiliano
Authors of the University:
FERRARA Massimiliano
Handle:
https://iris.unirc.it/handle/20.500.12318/150806
Full Text:
https://iris.unirc.it//retrieve/handle/20.500.12318/150806/402582/Ferrara_2024_AMS_Shapley%20Value_editor.pdf
Published in:
APPLIED MATHEMATICAL SCIENCES
Journal
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URL

https://www.m-hikari.com/ams/ams-2024/ams-9-12-2024/919155.html
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