How do you calibrate a model's probabilities (Platt scaling, isotonic, temperature)?
A model can rank perfectly yet lie about its probabilities; its 0.9 may hold only 70% of the time. What shows depth is knowing the three fixes, the data size each requires, and how to measure the gap. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A model can rank perfectly yet lie about its probabilities; its 0.9 may hold only 70% of the time. What shows depth is knowing the three fixes, the data size each requires, and how to measure the gap. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
