Comparing Strategies for Post-Hoc Explanations in Machine Learning Models Aabhas Vij and Preethi Nanjundan Abstract Most of the machine learning models act as black boxes, and hence, the need for interpreting them is rising. There are multiple approaches to understand the outcomes of a model. But in order to be able to trust the interpretations, there is a need to have a closer look at these approaches. This project compared three such frame- works—ELI5, LIME and SHAP. ELI5 and LIME follow the same approach toward interpreting the outcomes of machine learning algorithms by building an explainable model in the vicinity of the datapoint that needs to be explained, whereas SHAP works with Shapley values, a game theory approach toward assigning feature attribution. LIME outputs an R-squared value along with its feature attribution reports which help in quantifying the trust one must have in those interpretations. The R-squared value for surrogate models within different machine learning models varies. SHAP trades-off accuracy with time (theoretically). Assigning SHAP values to features is a time and computationally consuming task, and hence, it might require sampling beforehand. SHAP triumphs over LIME with respect to optimization of different kinds of machine learning models as it has explainers for different types of machine learning models, and LIME has one generic explainer for all model types. Keywords Interpretability · LIME · SHAP · Explainable AI · ELI5 1 Introduction Artificial intelligence (AI) is playing a major role in automating day–to-day tasks in the real world. There is no denying that many business decisions have heavy dependency on artificial intelligence [1]. The dependency is justified by the accuracy of the models AI runs on. In the earlier era of machine learning, the models were simpler and easier to explain [2]. As this era advances, the models are getting more A. Vij · P. Nanjundan (B ) Christ (Deemed To Be University), Lavasa, India e-mail: preethi.n@christuniversity.in A. Vij e-mail: aabhas.vij@science.christuniversity.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Shakya et al. (eds.), Mobile Computing and Sustainable Informatics, Lecture Notes on Data Engineering and Communications Technologies 68, https://doi.org/10.1007/978-981-16-1866-6_41 585