A proper scoring rule that quantifies the accuracy of probabilistic predictions by computing the mean squared difference between predicted probabilities and actual outcomes (0 or 1). For binary cases: Brier = (1/N) * Σ(pred_i - outcome_i)². It rewards honest probability reporting and penalizes overconfidence, serving as a calibration metric for classifiers. [formal: brier_score | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.18]
Accepted ontology entry
brier score
A proper scoring rule that quantifies the accuracy of probabilistic predictions by computing the mean squared difference between predicted probabilities and actual outcomes (0 or 1). For binary cases: Brier = (1/N) * Σ(pred_i - outcome_i)²…
Definition
Why it is in scope
A proper scoring rule for evaluating the accuracy of probabilistic predictions: it computes the mean squared difference between predicted probabilities and actual binary outcomes. Created by meteorologist Glenn Brier in 1950 for weather forecasting, it persists as a standard metric in classification model evaluation, embedded in statistical software packages and taught in machine learning curricula.
Names and aliases
- brier scoreen · CANONICAL
Relations from this entry
- cmsetc16w05vz3vv3qnp8u60aINSTANCE_OF →
TESTED INSTANCE_OF: brier score IS a specific kind of scoring rule. A scoring rule quantifies the accuracy of probabilistic predictions; the brier score is the mean squared error of such predictions across all possible outcomes. A competent speaker would call the brier score 'a scoring rule'.
- cmsf6w1ek06ms3vv3snhiwxwlDEPENDS_ON →
Brier score measures probabilistic forecast accuracy via mean((p-x)^2). Remove probability theory and it loses its mathematical foundation entirely.
Relations to this entry
No accepted relations in this direction.
Record identity
- Created
- Aug 4, 2026, 1:53 PM UTC
- Content hash
- af50cb1802a86320523e12df2f0c8a83b2eb8d168a1d96c0fd34d08c72dd9157