min-over-strata concept
| Definition | The rule that a verdict over several groups is the worst group's verdict, with abstention counted as a verdict, never the average. Chapter 10. Also abstention, abstain. |
|---|---|
| Example | Strata at 0.95, 0.93, 0.97, and 0.66 have a mean of 0.878 and a minimum of 0.66, and the minimum is the verdict. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id min-over-strata, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | Book equation 10.7. |
| Assumptions and scope |
|
| Prior art | none recorded |
| Evidence | turboquant-pro/docs/STRATA_RFC.md:24-98, turboquant-pro/docs/RESULTS_multilingual_strata.md:1-55, lean/DataMiningAsObservation/Certificate.lean |
| Reviewed | not yet reviewed; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
Book equation 10.7.
\[\text{verdict}=\min_{i:\ n_i\ge n_{\min},\ |Q_i|\ge q_{\min}}\ \mathrm{score}_i,\qquad \text{ABSTAIN otherwise}.\]
Book equation 14.5.
\[\mathrm{FC}_g=\Pr\big[y=\text{violation}\ \big|\ \hat y=\text{clear},\ g\big],\qquad \text{verdict}=\max_{g:\ n_g\ge n_{\min}}\mathrm{FC}_g,\qquad \text{ABSTAIN otherwise}.\]
Conditions
- A verdict over several groups is the worst group’s verdict, with a group too small to score counted as an abstention and reported, never averaged away.
- The first stratified run made two wrong predictions, and the record carries them at the size of the result.
Conditions are curated in entries.toml rather than read
from a record.
Ledger
none
First stated
The compression program’s stratified evaluation, STRATA, in turboquant-pro, DOI 10.5281/zenodo.20660087, and chapter 10 section 10.4 of Data Mining as Observation.
Measurements
| Where the book states it | Numbers, as the book’s sources table records them | Source |
|---|---|---|
| chapter 10 section 10.4 | area map, boundary rule, hash, refuse not warn, intra and transit counts, area classes, abstention rule | turboquant-pro/docs/STRATA_RFC.md:24-98 |
| chapter 10 section 10.4 | first run, 350000 of 2391361 rows, 7 of 14 eligible, abstentions with 2 and 1 rows, Robin Hood 0.3447 to 0.4469 ratio 1.30 against 1.5, skew 2.68 to 4.40 ratio 1.64 against 3, 63 times sample range | turboquant-pro/docs/RESULTS_multilingual_strata.md:1-55 |
Failures and corrections
none
Invariance envelope
none declared
Machine checked
lean/DataMiningAsObservation/Certificate.lean,
theorems falseClear_mul_coverage,
coverage_empty, falseClear_mem_unit,
minOverStrata_passes_iff,
minOverStrata_le_weighted_mean, at observation-data-mining
f3914f0; what the check covers is stated in the book’s appendix
C.
Used in
Data Mining as Observation primer S, chapters 0, 10, 11, 14.
Related
false-clear rate; coverage; hubness; certificate.
See also
Ledger rows that cite the entry’s records without naming it: NEG-14.
Sources-table rows that share a record with the entry without naming it: chapter 2 section 2.6, chapter 3 section 3.5, chapter 8 section 8.1, chapter 8 section 8.4, chapter 8 section 8.7, chapter 8 section 8.10, chapter 10 section 10.2, chapter 10 section 10.4, chapter 10 section 10.5, chapter 11 section 11.4, chapter 11 section 11.6, chapter 12 section 12.2, chapter 12 section 12.4, chapter 13 section 13.1.
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.