Model profile

RealTabPFN v2

TabPFN v2, by Prior Labs, is a pretrained transformer for classification and regression on tables. It takes labelled training rows as context and predicts new rows without training a separate neural network from scratch for each dataset.

Tabular FoundationRegular feature limit: 500Source ↗

Model profile

RealTabPFN v2

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	1,043	990–1,093	44
DNA methylation	1,208	1,081–1,446	4
GLM/PLM Embeddings	1,110	856–1,442	4
Gene expression	1,069	916–1,216	10
Genomic prediction	832	758–874	4
Metagenomics	1,065	988–1,143	10
Molecular properties	953	888–1,025	10
Other	1,221	1,187–1,363	2