Model profile

TabPFN Wide (8k)

TabPFN-Wide continues pre-training TabPFN 2 on synthetic tables with many features and few samples, a setting common in omics. This classification model uses the wide-v2-8k checkpoint to work with wider inputs than the original model.

Tabular FoundationNo regular feature limit specifiedSource ↗

Model profile

TabPFN Wide (8k)

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	1,017	936–1,087	32
DNA methylation	1,227	1,099–1,514	3
GLM/PLM Embeddings	1,168	855–1,489	3
Gene expression	1,117	976–1,238	10
Metagenomics	986	853–1,113	10
Molecular properties	826	601–979	5
Other	1,004	980–1,070	1