Model profile

TabDPT

TabDPT is a tabular foundation model by Layer6 AI pretrained on real datasets using self-supervised prediction tasks. For a new classification or regression problem, it retrieves relevant labelled rows and uses them as context to predict new rows without updating its weights on that dataset.

Tabular FoundationRegular feature limit: 2,500Source ↗

Model profile

TabDPT

Default parameters. Peer models use their prespecified benchmark-default configuration without per-dataset tuning. This card describes that configuration, not the best achievable tuned model.

Operating map

Macro-F1 Elo relative to Random Forest. Columns are training-sample caps; rows are dataset modalities. Small labels show the number of binding targets.

Elo difference vs. Random Forest

Fixed nonlinear scale; extremes capped at ±1,000 Elo.

No result / comparison unavailable

Reliability

Failure rate map

Dataset modality

Failed fits / (successful + failed fits) for the selected model and modality. Design skips are omitted. A dash means no attempted fits; 0% means all attempted fits succeeded. Hover for counts. Colours span 0–20%, with rates above 20% using the darkest shade; cell labels and hover retain the actual rate. This chart is independent of the performance sensitivity view.

Reference benchmark results

Training-data overlap: part of this benchmark was used in this model's training.

Strict Macro-F1 Elo at p=10,000, n=100. Random Forest is anchored at 1,000; intervals are target-bootstrap 95% intervals. These reference results stay fixed while you explore other budgets in the charts. Overlapping intervals do not establish a clear winner.

Dataset modality	Elo	95% interval	Targets
All modalities	1,038	988–1,091	44
DNA methylation	938	807–1,045	4
GLM/PLM Embeddings	1,203	1,053–1,491	4
Gene expression	1,073	1,000–1,173	10
Genomic prediction	851	724–917	4
Metagenomics	1,094	1,022–1,167	10
Molecular properties	978	842–1,111	10
Other	1,144	785–1,590	2