Matcher & dedup

Record matchingrecord matchingThe process of determining whether two database records refer to the same real-world entity. Mailwoman's matcher uses a geocode-first approach (match the resolved place, not the address string) with Fellegi-Sunter probabilistic scoring. and entity resolutionrecord matchingThe process of determining whether two database records refer to the same real-world entity. Mailwoman's matcher uses a geocode-first approach (match the resolved place, not the address string) with Fellegi-Sunter probabilistic scoring.: the Fellegi-SunterFellegi-SunterA probabilistic record linkage model that computes match probability from agreement-level log-likelihood ratios: log₂(m/u) where m is the probability of agreement given a true match and u is the probability of agreement by chance. Mailwoman learns m and u label-free via expectation-maximization. and learned scorers, dedup ceilings, and cross-source generalizationgeneralizationA trained model's performance on data unlike its training set — new regions, new input distributions. The property honest eval is designed to measure..