Codesota · Benchmark · OpenML-CC18Home/Leaderboards/Structured Data & Forecasting/Tabular Classification/OpenML-CC18
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OpenML-CC18.

Curated classification benchmark suite of 72 tabular datasets

Paper ↗Leaderboard ↓
§ 01 · Leaderboard

Results by metric.

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Accuracy

Accuracy is the reported evaluation metric for OpenML-CC18. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.

Higher is better

Trust tiers for Accuracyverifiedpapervendorcommunityunverified
RankModelTrustScoreYearLinksFix
01AutoGluon-Tabular
AutoGluon mean accuracy on OpenML-CC18. HPO tuned.
verified88.52025Paper ↗Looks wrong?
02TabPFN
TabPFN v1 mean accuracy on OpenML-CC18.
verified872025Paper ↗Looks wrong?
03LightGBM
LightGBM mean accuracy on OpenML-CC18. HPO tuned.
verified86.92025Paper ↗Looks wrong?
04XGBoost
XGBoost mean accuracy on OpenML-CC18. HPO tuned.
verified86.32025Paper ↗Looks wrong?
05Random Forest
Random Forest mean accuracy on OpenML-CC18. HPO tuned.
verified85.72025Paper ↗Looks wrong?
§ 04 · Submit a result

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