Comparative Analysis of Water Applicability Predictions Explained by The LightGBM Model Using SHAP and LIME

Abstract. This article mainly discusses the application of the LightGBM model to predict water potability for a dataset containing multiple water quality features. The focus of the study is to use two popular model interpretability techniques: SHAP and LIME to explain the model's prediction results. The results show that SHAP can globally explain the feature importance of the entire dataset and provide a deep understanding of the features and model behavior, while LIME provides a detailed explanation of a single prediction through local linear approximation, which is easier to interpret and apply. This article also compares the strengths and limitations of SHAP and LIME in explaining the LightGBM models behavior, demonstrating their applicability and explanatory power in different contexts. In addition, this article also explores the actual application scenarios of water quality prediction and analyzes how interpretability improves model transparency and trust in this field. Through these analyses, the article provides practical suggestions on how to choose appropriate model interpretation methods in reality.