Model profile

XGBoost (tuned)

This variant uses the same XGBoost tree boosting model, with settings selected from a small grid on a holdout drawn only from the training data. The grid varies tree depth, learning rate, feature sampling and regularization before refitting the selected configuration.

Gradient BoostingNo regular feature limit specifiedSource ↗

Model profile

XGBoost (tuned)

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	908	862–952	44
DNA methylation	1,027	721–1,242	4
GLM/PLM Embeddings	842	657–971	4
Gene expression	932	784–1,041	10
Genomic prediction	742	585–911	4
Metagenomics	911	845–962	10
Molecular properties	867	786–943	10
Other	991	893–1,080	2