Welcome to Interpretation, Tuning & Deployment, the final teaching module of the course. A model that scores well but that you cannot explain, tune, or ship is only half finished. This module covers the last mile from a trained model to something you can trust and deploy.

You’ll learn the three kinds of XGBoost feature importance — and why the default one can mislead — then move to SHAP, the method that gives honest, consistent explanations for both the whole model and individual predictions. You’ll tune systematically with Optuna instead of hand-guessing, compare XGBoost against LightGBM, and learn to save and reload a model so it can serve predictions. The module ends with a guided project that takes one model from training all the way to a documented, deployable artifact.

Every model, explanation, and tuning run here is executed for real with xgboost, shap, and optuna. Start with Lesson 1, where you’ll see why the importance scores everyone reads first can point you in the wrong direction.