Started okay, said something about predicted probabilities matching actual frequencies, which is the right intuition.
Start by defining model calibration as the alignment between predicted probabilities and observed frequencies, then explain how to measure it using reliability diagrams and metrics like ECE or Brier score. Finally, discuss practical improvement techniques such as post-hoc calibration methods (Platt scaling, isotonic regression) and their trade-offs, emphasizing when to apply them in a production ML pipeline.
Pro tip: Emphasize that calibration should be evaluated on a held-out validation set and that different calibration methods have varying data requirements and assumptions—choosing the right one depends on dataset size, model type, and business impact. Also, mention that calibration can degrade after model updates, so monitoring calibration drift is crucial.
Explain that calibration measures how well predicted probabilities reflect true likelihoods, e.g., among predictions with 0.8 confidence, 80% should be correct.
Describe common metrics: reliability diagrams (calibration curves), Expected Calibration Error (ECE), Maximum Calibration Error (MCE), and Brier score. Mention binning strategies and their limitations.
Discuss causes: model overconfidence/underconfidence, distribution shift, insufficient training data, or inherent model bias (e.g., neural networks often overconfident).
List methods: post-hoc techniques like Platt scaling (sigmoid), isotonic regression, temperature scaling for neural nets; also data augmentation, regularization, or ensembling. Mention trade-offs: isotonic needs more data, temperature scaling is simple but assumes a single parameter.
Stress the importance of evaluating calibration on a separate validation set, and setting up monitoring for calibration drift in production, especially for high-stakes decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.