You have built a model and shared promising evaluation results with your team. Before those findings are used to guide decisions, you need a clear way to show that the results are trustworthy and repeatable.
How do you ensure the validity and reliability of your findings?
Choosing evaluation methods that generalize beyond one splitUsing cross-validation and holdout testing appropriatelyChecking calibration, not just discriminationInterpreting confusion matrix trade-offs at a real threshold