Gene expression

mouseType

This dataset contains normalized gene expression measurements from DNA microarrays in the RSCTC 2010 challenge collection. The benchmark predicts one of seven supplied sample classes, although the OpenML documentation does not explain their biological meaning.

ClassificationLicence: PublicOpenML-1083Target: Decisionopenml ↗

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
Kumo Tabular (M)	0.7435	0.7412	0.7492	0.957
LimiX2	0.7115	0.6921	0.7465	0.9377
RealTabPFN 2.5	0.706	0.7097	0.7074	0.9482
TabPFN 3.5	0.6913	0.6903	0.6994	0.9439
Causilo	0.6845	0.6818	0.6778	0.9387
Logistic Regression	0.6737	0.6624	0.6717	0.9276
Logistic Regression (tuned)	0.6643	0.6465	0.6696	0.9222
TabPFN Wide 5k (ne3)	0.6575	0.6603	0.6517	0.9372
Random Forest (tuned)	0.5981	0.5538	0.5601	0.8986
MLP (tuned)	0.5769	0.5662	0.5959	0.9025
Extra Trees (tuned)	0.5642	0.5776	0.5545	0.9152
XGBoost (tuned)	0.5627	0.5507	0.5574	0.8799
MLP	0.5507	0.5583	0.5635	0.9026
TabM	0.5365	0.516	0.5429	0.8844
AutoGluon	0.5323	0.5904	0.5459	0.9213
RealMLP	0.5322	0.5217	0.5377	0.8968
Extra Trees	0.5316	0.5182	0.5074	0.8891
Random Forest	0.517	0.5094	0.4944	0.8995
XGBoost	0.4955	0.4784	0.4975	0.8545
GeneICL	0.4904	0.4815	0.5481	0.879
TabDPT	0.4535	0.5467	0.4616	0.92
MITRA	0.4365	0.3944	0.4297	0.8212
RealTabPFN v2	0.2878	0.3981	0.3409	0.8928
TabFM	0.2685	0.359	0.292	0.9146