Adds recall@k metric to rank eval API #52889
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This change adds the recall@k metric and refactors precision@k to match the new metric. Recall@k is an important metric to use for learning to rank (LTR) use-cases. Candidate generation or first ranking phase ranking functions are often optimized for high recall, in order to generate as many relevant candidates in the top-k as possible for a second phase of ranking. Adding this metric allows tuning that base query for LTR. See: #51676
joshdevins
added
>enhancement
:Search Relevance/Ranking
|
Pinging @elastic/es-search (:Search/Ranking) |
Signature changes in backport to 7.x
joshdevins
changed the title
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This change adds the recall@k metric and refactors precision@k to match
the new metric.
Recall@k is an important metric to use for learning to rank (LTR)
use-cases. Candidate generation or first ranking phase ranking functions
are often optimized for high recall, in order to generate as many
relevant candidates in the top-k as possible for a second phase of
ranking. Adding this metric allows tuning that base query for LTR.
See: #51676
Backports: #52577