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# Matching Metrics
## Precision/Recall
For fuzzy matching we want to understand precision and recall. Options for test datasets:
* manually curated (100s of examples); could determine
* autogenerate slightly different set of real-world metadata (e.g. crossref vs. doaj) converted to releases
* automatically distorted set of records; 1 original, plus N distorted (synthetic)
## Overall numbers
* number of clusters per clustering method: "title", "lowercase", "nysiis",
"sandcrawler", a few more - contrastive comparison of these cluster, e.g. how
many more matches/non-matches we get for the various methods
* take N docs from non-clusters and run verify; we would want 100% different/ambiguous results
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