Notes on Model Evaluation

Good model evaluation starts with a clear definition of the decision the model supports. Accuracy, calibration, fairness, and interpretability all matter differently depending on what happens after the prediction is made.

For future posts, this is a useful structure:

The question

What problem is the analysis trying to answer?

The data

What data is available, and what does it fail to capture?

The method

What model or analysis approach is appropriate, and why?

The takeaway

What should a reader remember or do differently?