Model profile

TabM

TabM, by Yandex Research, trains a group of multilayer perceptrons together for tabular classification or regression, with most of their weights shared. Each input produces several predictions that are combined, giving the model an ensemble structure without storing and training a full set of separate networks.

Deep LearningNo regular feature limit specifiedSource ↗

Model profile

TabM

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	1,007	965–1,057	44
DNA methylation	974	893–1,043	4
GLM/PLM Embeddings	1,131	1,049–1,334	4
Gene expression	1,114	987–1,262	10
Genomic prediction	886	669–1,156	4
Metagenomics	941	866–1,003	10
Molecular properties	982	914–1,070	10
Other	1,026	958–1,099	2