2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 8 , Issue 1 , PP: 60-70, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2
Doi: https://doi.org/10.54216/AJBOR.080106
In today’s paced and data centric world the integration of Artificial Intelligence (AI) technologies has become a game changer, in industries. However effectively utilizing AI to make informed decisions is still a task due to the complexities of datasets and the need for predictive models. This study aims to explore and evaluate Machine Learning (ML) classifiers such as Gradient Boosting, Light Gradient Boosting Machine (LightGBM) Extreme Gradient Boosting (XGBoost) and stacking classifiers within decision making scenarios. The objective is to assess their effectiveness in handling datasets and gain insights into their performance metrics for improving decision making processes. Comparative analysis of these classifiers reveals strengths and capabilities when applied in decision making contexts. The experimental findings highlight the potential of classifiers Gradient Boosting, in optimizing decision making even in complex situations.
Artificial Intelligence , Business operations , Machine Learning , Decision Making Operation Research.
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