Model explorer

Models

Compare linear models, tree ensembles, neural networks, AutoML, and tabular foundation models on biomedical classification and regression tasks. Open a model card to explore performance relative to Random Forest or another model across biological modalities, feature counts, and training-sample budgets, and identify where model fits fail.

ModelFamilyIdentifier Source Model details
AutoGluonAutoMLAUTOGLUONSource ↗Explore →
CatBoostGradient BoostingCATSource ↗Explore →
CausiloTabular FoundationCAUSILOSource ↗Explore →
ConstantBaselineDUMMYSource ↗Explore →
Extra TreesTree-basedXTSource ↗Explore →
Extra Trees (tuned)Tree-basedXT-TUNEDSource ↗Explore →
GeneICLTabular FoundationGENEICLSource ↗Explore →
KNNTraditional MLKNNSource ↗Explore →
Kumo Tabular (M)Tabular FoundationKUMO-TABULAR-MEDIUMSource ↗Explore →
LightGBMGradient BoostingGBMSource ↗Explore →
LimiX2Tabular FoundationLIMIX-2Source ↗Explore →
Logistic RegressionTraditional MLLRSource ↗Explore →
Logistic Regression (tuned)Traditional MLLR-TUNEDSource ↗Explore →
MITRATabular FoundationMITRASource ↗Explore →
MLPDeep LearningNN_TORCHSource ↗Explore →
MLP (tuned)Deep LearningNN_TORCH-TUNEDSource ↗Explore →
Random ForestTree-basedRFSource ↗Explore →
Random Forest (tuned)Tree-basedRF-TUNEDSource ↗Explore →
RealMLPDeep LearningREALMLPSource ↗Explore →
RealTabPFN 2.5Tabular FoundationREALTABPFN-V2.5Source ↗Explore →
RealTabPFN v2Tabular FoundationREALTABPFN-V2Source ↗Explore →
TabDPTTabular FoundationTABDPTSource ↗Explore →
TabFMTabular FoundationTABFMSource ↗Explore →
TabICL v2Tabular FoundationTABICLSource ↗Explore →
TabMDeep LearningTABMSource ↗Explore →
TabPFN 3Tabular FoundationTABPFN-V3Source ↗Explore →
TabPFN 3.5Tabular FoundationTABPFN-V3.5Source ↗Explore →
TabPFN Wide (8k)Tabular FoundationTABPFN-WIDESource ↗Explore →
TabPFN Wide 5k (ne3)Tabular FoundationTABPFN-WIDE-5K-NE3Source ↗Explore →
XGBoostGradient BoostingXGBSource ↗Explore →
XGBoost (tuned)Gradient BoostingXGB-TUNEDSource ↗Explore →