Gene expression

BurkittLymphoma

The BurkittLymphoma dataset contains normalized gene expression measurements from DNA microarrays. The benchmark predicts one of three supplied lymphoma sample classes; OpenML identifies these only by numeric labels and does not document the distinctions between them.

ClassificationLicence: PublicOpenML-1084Target: 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
TabPFN 3.5	0.9062	0.8765	0.8978	0.9862
LimiX2	0.8992	0.8527	0.9067	0.9795
Causilo	0.8838	0.846	0.8808	0.9831
RealMLP	0.8779	0.8344	0.8654	0.9721
RealTabPFN 2.5	0.8646	0.8324	0.8612	0.9841
Kumo Tabular (M)	0.8635	0.8221	0.8593	0.9779
TabPFN Wide 5k (ne3)	0.8605	0.8318	0.8571	0.9859
RealTabPFN v2	0.8455	0.7992	0.8289	0.9812
TabDPT	0.8363	0.7905	0.8367	0.9612
MLP	0.8355	0.7897	0.8256	0.9598
AutoGluon	0.8126	0.7668	0.8167	0.9644
MITRA	0.812	0.7674	0.8103	0.9674
Extra Trees (tuned)	0.8083	0.7588	0.7893	0.968
Logistic Regression	0.8082	0.778	0.8097	0.9736
Logistic Regression (tuned)	0.7988	0.7601	0.798	0.9689
Random Forest	0.795	0.7494	0.7756	0.9643
Random Forest (tuned)	0.7886	0.7266	0.7731	0.9581
MLP (tuned)	0.7825	0.7445	0.7801	0.9595
TabM	0.7784	0.7233	0.7622	0.9451
GeneICL	0.7496	0.6886	0.7611	0.9407
XGBoost	0.7085	0.6378	0.7055	0.8959
Extra Trees	0.698	0.6849	0.7126	0.9588
TabFM	0.6633	0.622	0.6745	0.9583
XGBoost (tuned)	0.6182	0.5623	0.6376	0.8898