Model profile

RealMLP

RealMLP is a multilayer perceptron trained on each dataset for classification or regression. It combines numerical preprocessing with a carefully chosen network and training setup, using defaults selected across a broad collection of tabular tasks.

Deep LearningNo regular feature limit specifiedSource ↗

Model profile

RealMLP

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	1,005	937–1,068	44
DNA methylation	986	830–1,099	4
GLM/PLM Embeddings	1,152	1,014–1,314	4
Gene expression	1,174	1,063–1,303	10
Genomic prediction	643	241–883	4
Metagenomics	1,018	938–1,072	10
Molecular properties	905	734–1,078	10
Other	1,009	777–1,213	2