Vol. 1 No. 1 (2025), Articles
Vol. 1 No. 1 (2025)

Income Level Estimation with Light-GBM: Understanding Model Decisions with Explainable AI Techniques Shap and Lime

Articles
Published 2025-07-30
Cem Özkurt
Sakarya University of Applied Sciences
https://orcid.org/0000-0002-1251-7715
Fatmir Garri
Sakarya University of Applied Sciences
https://orcid.org/0009-0005-6762-6846
Bilal Emre Yahyaoğlu
Sakarya University of Applied Science
https://orcid.org/0009-0000-5113-9959
Onur Ağca
Sakarya University of Applied Science
https://orcid.org/0009-0008-7833-0540
Necip Furkan Bildiren
Sakarya University of Applied Science
https://orcid.org/0009-0000-8673-6462
Sergen Kaynak
Sakarya University of Applied Science
https://orcid.org/0009-0000-6886-2019
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Keywords

Revenue prediction
Machine learning
Explainable artificial intelligence
SMOTE

How to Cite

Income Level Estimation with Light-GBM: Understanding Model Decisions with Explainable AI Techniques Shap and Lime. (2025). Artificial Intelligence in Applied Sciences, 1(1), 7-12. https://doi.org/10.69882/adba.ai.2025072
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Abstract

This study examines the use of machine learning and artificial intelligence algorithms to predict individuals' annual incomes. In analyses conducted using the Python programming language, the best performance was achieved in models utilizing the "Synthetic Minority Over-sampling Technique (SMOTE)" for imbalanced data sets, with an accuracy of 87.45%, precision of 85.74%, recall of 89.31%, and an F1 score of 87.30, using the "Light Gradient Boosting Machines" algorithm. Additionally, the impact of parameters and variables on income prediction was examined using interpretable artificial intelligence algorithms. The results of the study emphasize the importance of employing effective methods and explaining machine learning model predictions, as well as addressing imbalanced data sets.

 

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