AI & Data Science

Model Evaluation Concepts

Ideas that come up once you have a working model and need to know whether to trust it i.e. calibrating raw scores into real probabilities, judging those probabilities with the Brier score, and explaining individual predictions with SHAP.

Part 1Calibration: turning a ranking into a probability you can actually trustWhy class-weighted scores lie about probability, and how Platt scaling and isotonic regression fix it.Part 2The Brier score: Helps in Model Evaluation for Probabilistic ClassifiersA working example of the Brier score, why it punishes confident wrong answers, and how it helps in model comparison, calibration, and monitoring.Part 3Opening the black box: explaining the model with SHAPWhat SHAP actually measures, a real customer's score broken down feature by feature, and three different views of what matters most.