You've built a model and the team wants confidence that its results will hold up outside the development environment. You need a clear way to judge whether the model is trustworthy before people use it to make decisions.
How do you ensure the validity of your statistical models?
Performance should generalize beyond one train-test splitPredicted probabilities should be calibrated if scores drive decisionsErrors should be acceptable for the business contextResults should hold across important segments and over time