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Model Training Blocking Rules - Splink

moj-analytical-services.github.io · 558 words · saved by 1 readers

Model Training Blocking Rules choose which record pairs from a dataset get considered when training a Splink model. These are used during Expectation Maximisation (EM), where we estimate the m probability (in most cases). The aim of Model Training Blocking Rules is to reduce the number of record pairs considered when training a Splink model in order to reduce the computational resource required. Each Training Blocking Rule define a training "block" of records which have a combination of matches and non-matches that are considered by Splink's Expectation Maximisation algorithm. The Expectation Maximisation algorithm seems to work best when the pairwise record comparisons are a mix of anywhere between around 0.1% and 99.9% true matches. It works less efficiently if there is a huge imbalance between the two (e.g. a billion non matches and only a hundred matches). Note Unlike Prediction Rules, it does not matter if Training Rules excludes some true matches - it just needs to generate examp

Blocking for Model Training ¶ Model Training Blocking Rules choose which record pairs from a dataset get considered when training a Splink model. These are used during Expectation Maximisation (EM), where we estimate the m probability (in most cases). The aim of Model Training Blocking Rules is to reduce the number of record pairs considered when training a Splink model in order to reduce the computational resource required. Each Training Blocking Rule define a training "block" of records which have a combination of matches and non-matches that are considered by Splink's Expectation Maximisati

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