Tag
calibration
- Apr 27, 2026 Score Calibration for Blended Ranking Why a ranker optimized for ordering produces scores with no absolute meaning, how to fit a monotone calibration, and what breaks when the query mix shifts. information retrieval
- Nov 4, 2025 Class Imbalance, Class Priors, and Cost-Sensitive Thresholds Why resampling and thresholding both leave the fitted probabilities unchanged or wrong, how a prior shift translates into an intercept adjustment, and where the expected-cost optimum sits. machine learning
- Jul 6, 2025 Calibrating Interval Estimates Against Recorded Outcomes What an eighty percent interval promises, how coverage is measured on small samples, why width must be scored alongside the hit rate, and how reference classes supply the correction. software engineering
- May 2, 2024 Softmax Coupling versus One-vs-Rest Probabilities What the normalising constant does to multiclass estimates, why one-vs-rest scores need not sum to one, and how the two parameterisations differ under class imbalance. machine learning
- Mar 11, 2024 Proper Scoring Rules and Why They Cannot Be Gamed Why some losses reward honest probability forecasts while others reward sharpness alone, what decompositions reveal about calibration, and why the logarithmic score is the only local strictly proper rule. machine learning
- Dec 29, 2023 Calibrating Scores Before Choosing an Operating Point How to measure whether a score means what it claims, why Platt scaling and isotonic regression behave differently at the ends, and what calibration cannot fix. machine learning
- Aug 4, 2022 Range Calibration: Trading Clipping Error Against Rounding Error How the clipping threshold is chosen for a quantized tensor, why the minimum-error range is not the observed range, and how percentile and divergence criteria differ in what they preserve. inference systems