INTERPRETATION OF MACHINE LEARNING ALGORITHMS FOR DECISION-MAKING IN RETAIL
Abstract
The interpretation of machine learning algorithms for decision-making in the retail industry is a highly relevant and important topic in the field of artificial intelligence. The rapid advancement of machine learning technologies has made it possible to analyze large amounts of data and make predictions with great accuracy. However, this has also led to a growing need for methods that can help explain and interpret the results of these predictions. In this research paper, we focus on the SHAP method as a promising solution to the challenge of interpreting machine learning algorithms. We begin by providing an overview of the latest research studies that support the importance of this issue. We then delve into the theoretical foundations of the SHAP method and its practical applications. To demonstrate the effectiveness of the SHAP method, we develop a model using the M5 Accuracy competition dataset, which was aimed at accurately predicting Walmart's hierarchical unit sales. As an example, we used LightGBM which is a gradient-boosting framework that uses tree-based learning algorithms. Also, we describe the used machine learning workflow with feature engineering of time series (including rolling and expanding window statistics) and category data. The model was rapidly interpreted using the SHAP approach, providing valuable insights into the decision-making process in the retail industry. Additionally, we highlight the limitations of existing methods and outline potential directions for future research. This is crucial in order to continue advancing the field of machine learning and ensuring its successful application in the retail industry. The interpretation of machine learning algorithms is crucial for making informed and effective decisions in the retail industry. Our work aims to contribute to the ongoing conversation and research surrounding this important topic. In conclusion, this research paper provides a comprehensive introduction to the interpretation of machine learning algorithms for decision-making in the retail industry. We hope that our work will contribute to a deeper understanding of this important issue and facilitate the effective application of machine learning in the retail industry.
References
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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.
