Model profile

XGBoost

XGBoost combines decision trees through gradient boosting, adding trees to improve the ensemble's predictions. Its training objective includes penalties on tree complexity, alongside the loss for classification or regression.

Gradient BoostingNo regular feature limit specifiedSource ↗

Model profile

XGBoost

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	879	832–924	44
DNA methylation	993	832–1,144	4
GLM/PLM Embeddings	811	635–906	4
Gene expression	896	797–978	10
Genomic prediction	530	314–716	4
Metagenomics	923	858–1,011	10
Molecular properties	866	783–936	10
Other	795	670–840	2