A stacked Ridge + XGBoost + LightGBM ensemble on Ames houses reaches 0.126 RMSLE, beating a Ridge baseline by about 8 percent. The stack weights are unbalanced, and that's fine.

A fraud classifier reaches 0.98 ROC-AUC on the famous ULB dataset and still ships a policy most fraud teams would reject. The 0.5 decision threshold is the bug.

A practical look at the California housing prices dataset: what the rows actually mean, why the target is capped, and what this dataset is good for teaching.