A research pipeline for predicting Codeforces problem ratings using metadata, solved statistics, and lightweight statement features, with transparent documentation of historical errors and a corrected evaluation framework.
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5
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MIT
2026-07-13
The project stands out for its rigorous self-audit, public erratum, and reproducible pipeline, offering a valuable case study in transparent ML research for educational data.
A research pipeline for predicting Codeforces problem ratings from official API metadata, solved statistics, exposure-aware features, and lightweight statement-structure features. Historical paper artifacts remain available for transparent comparison with the corrected evaluation pipeline.
Research status (updated 2026-07-12): Earlier paper results are retained for transparency and should be interpreted as retrospective. A code audit identified a split-configuration mismatch and test-informed model selection; the corrected controls and scope are documented in the public erratum. No corrected rerun has replaced the legacy full-API headline metrics. A separate locked historical statement-only backtest is now reported below.
If you only want the main idea, key results, and how to read the project quickly, see QUICK_OVERVIEW.md.
Cold-start note: Cold-start here means no solved-count behavior, not necessarily strict pre-contest prediction. Tags, metadata, and statement availability may not exactly match a real pre-contest setting.
Useful companion notes:
| Experiment | Main setting | Legacy reported result | Current interpretation |
|---|---|---|---|
| Contest-grouped prediction | HGB full API | MAE 166.9, within +/-200 = 69.7% | Legacy descriptive evaluation |
| Forward-time prediction | RF full API | MAE 152.5, within +/-200 = 71.2% | Legacy descriptive evaluation |
| Solved-only baseline | solved-count only | MAE 227-274 | Hypothesis-generating comparison |
| Cold-start prediction | metadata only | MAE 318-332 | Legacy descriptive evaluation |
| Statement-structure extension | metadata + text-light | MAE improves by 33.0-42.3 | Requires future confirmation |
| Rolling temporal validation | full API + age norm | average MAE 146.0 | Internal temporal analysis, not the future test |
A reproducible machine-learning project predicting Codeforces problem difficulty from public API metadata and solved statistics.
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