How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown
In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract […]