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

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.

Key Sources