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# Results Relevance

Eduction returns entities based on the extraction rules from the grammars and dictionaries. Eduction provides a test mode to measure extraction relevance precision and recall. Precision and recall are based on the comparison between human-marked results and engine-marked results. The following terms describe result relevance as used in Eduction.

- True Positives (TP). Human-marked results that are also marked by the engine. These results specify that an entity returned by the engine has also been marked as true by the person marking the document.
- False Positives (FP). Engine-marked results that are not marked by a human. These results specify that an entity returned by the engine has not been marked as true by the person marking the document.
- True Negatives (TN). Results that are not marked either by the person marking the document, or the engine.
- False Negatives (FN). Human-marked results that are not marked by the engine. These results specify that an entity not returned by the engine has been marked as true by the person marking the document.

From these relevance terms, you can determine precision and recall as follows:

- Recall is the percentage of true relevant entities that are extracted by an extraction rule, that is,

      TP / (TP + FN) * 100

- Precision is the percentage of extracted entities that are true entities, that is,

      TP / (TP + FP) * 100

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