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probabilities

Applied AI interview questions tagged probabilities, across every topic.

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Concepts behind "probabilities"

The curriculum that explains the ideas these questions test.

Core
📊 Evaluation & ML FoundationsSign in
Calibration and UncertaintyA model is calibrated if its confidence matches reality: among predictions it makes at 0.8, about 80% are correct. Modern neural nets (and LLMs) are typically overconfident, so raw scores are not trustworthy probabilities. You fix it post-hoc with temperature scaling, Platt scaling, or isotonic regression on a held-out set, and measure it with reliability diagrams and Expected Calibration Error. Applied-AI interviews probe it because any decision made on a probability (thresholds, expected value, abstention) is only as good as the calibration.
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