Welcome to Training Robust Models, the third module of the course. Knowing XGBoost’s knobs is not the same as training a model you can trust. Real datasets are imbalanced, have missing values, and mix numbers with categories — and this module teaches you to handle all of it with confidence.

You’ll use early stopping so you never have to guess how many trees to grow, and cross-validation with xgb.cv for honest performance estimates that do not depend on one lucky split. You’ll learn why accuracy lies on imbalanced data and how scale_pos_weight and the right evaluation metrics fix it, and you’ll see how XGBoost handles missing values and categorical features natively — no manual imputation or one-hot encoding required. A guided project closes the module by combining all of these into one robust training pipeline.

Every model here is trained for real, mostly on the Adult Income dataset with its genuine class imbalance and categorical columns. Start with Lesson 1, where you’ll let the model tell you when to stop training.