Brier Score
Definition
The Brier score is a strictly proper scoring rule for probabilistic forecasts. For binary events with forecast probabilities and outcomes , it is the mean squared error
For multiclass forecasts with probabilities and one-hot outcomes , the usual categorical form is
Why It Matters
It gives a simple way to evaluate probabilistic predictions while still reminding us that forecast quality is not only about top-line accuracy. The score rewards truthful probability estimates, but its value also depends on how informative or resolving the forecasts are.
Formalism / Key Objects
- Canonical definition: binary Brier score is , with lower values better.
- Key decomposition: for binned binary forecasts, Murphy’s decomposition writes the score as , separating reliability, resolution, and base-rate uncertainty.
- Skill score: relative to a reference forecast, .
- Worked contrast: always predicting the base rate can look well calibrated while having poor resolution; see Brier Decomposition.
Connections
- Closely tied to Calibration but not reducible to it.
- Useful in AI Evaluation and Benchmarking when confidence quality matters alongside correctness.
- Lives inside the broader probabilistic-evaluation toolkit tracked by Probability and Statistics.
- Brier Decomposition holds the fuller formal payload for decomposition, skill scores, and calibration-versus-resolution tradeoffs.
Common Confusions
- A low Brier score does not mean calibration alone is good; refinement and base-rate structure also matter.
- The score is natural for binary and categorical events, not ordinal targets.
- The common modern binary range is , but Brier’s original 1950 multicategory formulation used a scale up to .
- Proper scoring rules evaluate forecasts, not just decisions taken from them.