ІНТЕРПРЕТАЦІЯ АЛГОРИТМІВ МАШИННОГО НАВЧАННЯ ДЛЯ ПРИЙНЯТТЯ РІШЕНЬ У РОЗДРІБНІЙ ТОРГІВЛІ
Анотація
Стаття присвячена питанню інтерпретації алгоритмів машинного навчання для прийняття рішень у роздрібній торгівлі. Було описано останні дослідження, які підтверджують актуальність проблематики. Зосереджено увагу на підході SHAP як на одному з перспективних методів розв’язання поставленої задачі. Розглянуто як теоретичні засади, так і практичне використання даного підходу. Розроблено модель машинного навчання на базі набору даних змагань «M5 Accuracy», метою якого був точний прогноз продаж товарних одиниць для однієї з найбільших компаній роздрібної торгівлі у світі – Walmart. Побудована високорівнева інтерпретація розробленої моделі з використанням підходу SHAP. Також вказані напрямки майбутніх досліджень та обмеження використаних підходів.
Посилання
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S. Sharma, N. Islam, G. Singh and A. Dhir, "Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems?," in IEEE Transactions on Engineering Management, doi: 10.1109/TEM.2022.3157976
Weber, F.D. and Schütte, R. (2019), "State-of-the-art and adoption of artificial intelligence in retailing", Digital Policy, Regulation and Governance, Vol. 21 No. 3, pp. 264-279. https://doi.org/10.1108/DPRG-09-2018-0050
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Chu, C.-W., & Zhang, G. P. (2003). A comparative study of linear and nonlinear models for aggregate retail sales forecasting. In International Journal of Production Economics (Vol. 86, Issue 3, pp. 217–231). Elsevier BV. https://doi.org/10.1016/s0925-5273(03)00068-9
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1, 206–215 (2019). https://doi.org/10.1038/s42256-019-0048-x
Lundberg, S., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1705.07874
A Value for N-Person Games. (1952). RAND Corporation. https://doi.org/10.7249/p0295
Sajja, S., Aggarwal, N., Mukherjee, S., Manglik, K., Dwivedi, S., & Raykar, V. (2021). Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail. In Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD). CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD. ACM. https://doi.org/10.1145/3430984.3430995
Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. In Expert Systems with Applications (Vol. 210, p. 118354). Elsevier BV. https://doi.org/10.1016/j.eswa.2022.118354
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. In International Journal of Forecasting (Vol. 38, Issue 4, pp. 1346–1364). Elsevier BV. https://doi.org/10.1016/j.ijforecast.2021.11.013
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: a highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 3149–3157.
