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4. Estimating model parameters - Splink

moj-analytical-services.github.io · 3,006 words · saved by 1 readers

In the last tutorial we looked at how we can use blocking rules to generate pairwise record comparisons. Now it's time to estimate a probabilistic linkage model to score each of these comparisons. The resultant match score is a prediction of whether the two records represent the same entity (e.g. are the same person). The purpose of estimating the model is to learn the relative importance of different parts of your data for the purpose of data linking. For example, a match on date of birth is a much stronger indicator that two records refer to the same entity than a match on gender. A mismatch on gender may be a stronger indicate against two records referring than a mismatch on name, since names are more likely to be entered differently. The relative importance of different information is captured in the (partial) 'match weights', which can be learned from your data. These match weights are then added up to compute the overall match score. The match weights are are derived from the m a

Specifying and estimating a linkage model ¶ In the last tutorial we looked at how we can use blocking rules to generate pairwise record comparisons. Now it's time to estimate a probabilistic linkage model to score each of these comparisons. The resultant match score is a prediction of whether the two records represent the same entity (e.g. are the same person). The purpose of estimating the model is to learn the relative importance of different parts of your data for the purpose of data linking. For example, a match on date of birth is a much stronger indicator that two records refer to the sa

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