FREE LESSON · AI & machine learning · 3 OF 4
Machine Learning Evaluation and Calibration
Evaluation is an argument about the future — Generalization, metrics, calibration, and uncertainty
A held-out score is credible only when the split resembles the claim.
Random splits can leak people, time, devices, or near-duplicates across train and test. Temporal or group-based splits better simulate many deployments. Precision and recall describe different error tradeoffs; calibration asks whether predicted probabilities match frequencies; confidence intervals reflect sampling uncertainty. Slice analysis finds failures hidden by an average.
Every metric should name the population, threshold, time window, and consequence it represents.
Calibration and ranking answer different questions
A model can rank risky cases well but output probabilities that are systematically too high. Ranking metrics may look good while a “0.8” score corresponds to only 0.5 observed frequency. If decisions multiply probability by cost, calibration matters directly and should be checked over time and subgroups.
A score becomes a probability claim only after calibration evidence supports that interpretation.