Interpreting SHAP Values for Deep Learning Models
Deep learning models have revolutionized machine learning applications across industries, from medical diagnosis to financial forecasting. However, their complex architectures often make them “black boxes,” leaving practitioners struggling to understand why a model makes specific predictions. SHAP (SHapley Additive exPlanations) values have emerged as one of the most powerful tools for interpreting these intricate models, … Read more