2.2.4 Model interpretation
Machine learning models can be difficult to interpret, but the SHapley Additive explanations (SHAP) method, which is based on game theory and was proposed by Lundberg, et al.(Lundberg & Lee, 2017), can help to overcome this challenge by providing accurate explanations of the model’s output. The SHAP method ranks the importance of each feature in the input data based on its SHAP value, where a higher SHAP value indicates a greater positive impact on the machine learning model, while a lower SHAP value indicates a smaller impact or even a negative impact.