rerank instrument
| Definition | Reordering a candidate list with a second scorer. Its recall is at most the candidate coverage. Chapter 10 section 10.5. |
|---|---|
| Example | Reranking 100 candidates that hold 70 of the true neighbours can reach recall 0.7 and no more. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id rerank, kind instrument. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | none |
| Assumptions and scope |
|
| Prior art | none recorded |
| Evidence | turboquant-pro/docs/RESULTS_strata_phase23_gates.md:45-110, lean/DataMiningAsObservation/Rerank.lean |
| Reviewed | not yet reviewed; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
none
Conditions
- Reordering a candidate list with a second scorer. A rerank cannot return a row the list does not hold, so its recall is at most the candidate coverage, an oracle rerank reaches the coverage exactly, and a deeper list can only raise the coverage.
- Reranking the compressed candidates read 0.6627, identical to the unreranked recall, because candidate coverage was the wall, and the depth curve from 51 to 501 moved coverage from 0.696 to 0.926.
Conditions are curated in entries.toml rather than read
from a record.
Ledger
none
First stated
Chapter 10 section 10.5 of Data Mining as Observation, with
the candidate-coverage finding in
turboquant-pro/docs/RESULTS_strata_phase23_gates.md.
Measurements
| Where the book states it | Numbers, as the book’s sources table records them | Source |
|---|---|---|
| chapter 10 section 10.5 | rerank identical 0.6627, candidate coverage, depth curve 51 to 501, coverage 0.696 to 0.926, fidelity 0.663 to 0.702, originals rerank 0.923, 4096 bytes on 148 | turboquant-pro/docs/RESULTS_strata_phase23_gates.md:45-110 |
Failures and corrections
none
Invariance envelope
none declared
Machine checked
lean/DataMiningAsObservation/Rerank.lean,
theorems hits_le_coverage, recall_le_coverage,
oracle_rerank, coverage_mono, at
observation-data-mining f3914f0; what the check covers is stated in the
book’s appendix
C.
Used in
Data Mining as Observation chapters 4, 10, 11, 12.
Related
recall at k; anti-hub recall; inverted file; rank certificate; retrieval-augmented pipeline.
See also
Book equations stated beside the entry’s terms, not defining it: 11.3, 10.7, 12.3.
Ledger rows that cite the entry’s records without naming it: NEG-14, GO-B-Llama, GO-B-Llama-rematch.
Sources-table rows that share a record with the entry without naming it: chapter 4 section 4.5, chapter 10 section 10.4, chapter 10 section 10.5, chapter 11 section 11.4, chapter 12 section 12.4.
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.