Gene expression

Uterus vs. Other Tissue

These microarray gene expression profiles come from human tumor samples collected by the Expression Project for Oncology and assembled in GEMLeR. The benchmark predicts whether a sample comes from the uterus or from another tissue.

Binary classificationLicence: PublicOpenML-1138Target: Tissueopenml ↗

At this operating point

Model comparison

p=10,000, n=100 · Higher Macro-F1 is better

Random Forest is marked with a diamond. Models are ordered by the selected metric.

Across sample budgets

Learning curves

Models

All metric values at this operating point

Model	Macro-F1	MCC	Balanced accuracy	ROC-AUC
MLP (tuned)	0.7517	0.5163	0.7512	0.8553
MLP	0.7482	0.5029	0.747	0.8485
Kumo Tabular (M)	0.7069	0.4436	0.6945	0.8908
TabM	0.7009	0.4279	0.7418	0.8523
TabDPT	0.6904	0.3978	0.6948	0.8123
RealMLP	0.6867	0.4122	0.6949	0.8598
AutoGluon	0.684	0.3981	0.7017	0.824
LimiX2	0.684	0.3899	0.7367	0.8852
RealTabPFN 2.5	0.6795	0.383	0.6571	0.905
GeneICL	0.6773	0.38	0.6683	0.8677
TabPFN Wide 5k (ne3)	0.6754	0.3819	0.6534	0.9031
Logistic Regression	0.6678	0.3585	0.6417	0.8803
TabPFN 3.5	0.6558	0.3489	0.6339	0.8938
RealTabPFN v2	0.62	0.2777	0.5978	0.9009
Logistic Regression (tuned)	0.6141	0.3503	0.7229	0.8705
MITRA	0.5641	0.2088	0.5506	0.788
Extra Trees (tuned)	0.5316	0.1063	0.5368	0.8796
XGBoost (tuned)	0.5108	0.07072	0.5193	0.7215
Random Forest	0.4951	0.07768	0.5082	0.8454
Causilo	0.4791	0	0.5	0.875
Random Forest (tuned)	0.4791	0	0.5	0.8351
TabFM	0.4791	0	0.5	0.8837
XGBoost	0.4791	0	0.5	0.7061
Extra Trees	0.4791	0	0.5	0.8592