Experiment with a fixed threshold #7519
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Make sure we get near-perfect recall (ATM-light) and bad precision.
Absent features are now represented implicitly by the absence of a row in the `tokenFeatures` relation, rather than explicitly by an empty string. This leads to improved runtime performance. To enable this implicit representation, we pass the set of supported token features to the `scoreEndpoints` HOP. Requires CodeQL CLI v2.7.4.
This reverts commit ff1d0cc.
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After all the changes made on the modeling side, there is now better separation in scores between TPs and FPs.
This PR experiments with setting a hard-coded score threshold > 0.5 to determine which alerts to surface, rather than always selecting the max-likelihood class, thereby reducing FPs.
Replaces #7512, because it turns out I need to update the
worserather thancurrentlibraries, since the end-to-end evaluation runs the boosted queries on the worse modeling.The text was updated successfully, but these errors were encountered: