Model profile

TabFM

TabFM is Google's pretrained model for classification and regression on tables containing numerical and categorical columns. It predicts from labelled training rows supplied as context, using sampled groups of rows and features when needed, rather than updating its model weights for each new dataset.

Tabular FoundationRegular feature limit: 2,000Source ↗

Model profile

TabFM

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	1,015	952–1,081	44
DNA methylation	1,088	888–1,399	4
GLM/PLM Embeddings	1,228	1,053–1,517	4
Gene expression	848	699–979	10
Genomic prediction	908	525–1,353	4
Metagenomics	1,091	1,010–1,170	10
Molecular properties	986	833–1,160	10
Other	1,143	1,014–1,385	2