Model profile

Kumo Tabular (M)

Kumo Tabular, by NVIDIA, learns from synthetic tables before it is applied to a new dataset. It builds representations of columns and rows, then uses labelled examples as context for classification or regression; this benchmark uses the medium-sized model.

Tabular FoundationNo regular feature limit specifiedSource ↗

Model profile

Kumo Tabular (M)

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	1,225	1,156–1,291	44
DNA methylation	1,285	1,201–1,407	4
GLM/PLM Embeddings	1,455	1,343–1,723	4
Gene expression	1,366	1,273–1,479	10
Genomic prediction	1,059	846–1,369	4
Metagenomics	1,199	1,128–1,308	10
Molecular properties	1,113	928–1,278	10
Other	1,327	1,199–1,559	2