Model profile

MLP

This multilayer perceptron uses AutoGluon's PyTorch implementation for tabular classification and regression. It passes the input features through fully connected hidden layers with nonlinear activations to learn the prediction function.

Deep LearningNo regular feature limit specifiedSource ↗

Model profile

MLP

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	1,036	982–1,092	44
DNA methylation	929	775–1,078	4
GLM/PLM Embeddings	1,216	1,118–1,445	4
Gene expression	1,155	1,055–1,284	10
Genomic prediction	834	467–1,178	4
Metagenomics	1,085	1,028–1,141	10
Molecular properties	931	855–1,017	10
Other	996	832–1,140	2