Model profile

TabPFN 3

TabPFN 3, by Prior Labs, is a transformer pretrained on synthetic prediction tasks. For classification or regression on a new table, it uses the labelled training rows as examples to guide its predictions rather than learning the task from scratch.

Tabular FoundationRegular feature limit: 10,000Source ↗

Model profile

TabPFN 3

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

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	1,072	1,023–1,118	44
DNA methylation	1,062	823–1,233	4
GLM/PLM Embeddings	1,128	945–1,339	4
Gene expression	1,150	1,002–1,287	10
Genomic prediction	939	801–1,043	4
Metagenomics	1,092	1,014–1,167	10
Molecular properties	988	926–1,045	10
Other	1,301	1,293–1,391	2