Match weight

A match weight is what one comparison contributes to a pair's score. In the classical model the weight of an agreeing field is log2(m/u) and the weight of a disagreeing field is log2((1−m)/(1−u)). Because they are logs, weights add, and the total is a log-likelihood ratio which converts to a probability. The scale is intuitive once used: each additional unit doubles the odds of a match.

Reading a weight table is the fastest way to understand a matching system's behaviour, and the fastest way to find its bugs. A feature that fires on almost every pair carries little information regardless of how important it feels; a feature with a large negative weight can veto an otherwise convincing pair, which is correct for a conflicting registration number and wrong for a differing postcode.

Fitted weights need an out-of-fold check, because a weight fitted on pairs whose siblings appear in the same fold measures memorisation. Grouping by entity family before splitting is the standard defence, and a system whose weights were fitted without it is reporting a number about its own training set.

Entity Bind publishes its fitted weights, the fold structure used to fit them, and the features held out of the fit because they had too few observations to support a coefficient. A held feature keeps a fixed decisive penalty and is labelled as held, so a reader can tell which weights came from data and which were set by hand.