Gene expression

AP_Ovary_Lung

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 each sample comes from ovary or lung tissue.

Binary classificationLicence: PublicOpenML-1140Target: 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
Causilo	0.9477	0.896	0.9465	0.9754
LimiX2	0.9444	0.89	0.9439	0.969
TabPFN 3.5	0.9407	0.8826	0.937	0.9732
RealTabPFN v2	0.9371	0.8771	0.9319	0.9748
XGBoost (tuned)	0.9361	0.8776	0.9287	0.9536
Logistic Regression	0.9349	0.8704	0.935	0.9648
Kumo Tabular (M)	0.9315	0.8639	0.931	0.9724
TabPFN Wide 5k (ne3)	0.9311	0.8637	0.9281	0.9744
RealTabPFN 2.5	0.9279	0.857	0.9255	0.9734
Random Forest	0.9208	0.8447	0.9148	0.9703
MLP	0.9189	0.8387	0.9193	0.9568
GeneICL	0.9188	0.8379	0.9181	0.963
Random Forest (tuned)	0.9167	0.8379	0.9096	0.9662
XGBoost	0.9165	0.8373	0.9092	0.9515
TabDPT	0.9155	0.8336	0.9154	0.9562
MLP (tuned)	0.9116	0.8264	0.9113	0.9573
Extra Trees	0.9107	0.8248	0.9045	0.9691
Logistic Regression (tuned)	0.9098	0.8223	0.9119	0.9622
Extra Trees (tuned)	0.9073	0.8183	0.9004	0.9634
MITRA	0.906	0.8156	0.9077	0.9646
AutoGluon	0.9058	0.8148	0.9063	0.9585
TabM	0.9	0.8019	0.9043	0.9556
RealMLP	0.8995	0.8062	0.8965	0.9619
TabFM	0.7886	0.656	0.7877	0.967