Model profile

Logistic Regression

Logistic regression learns a weighted combination of the input features and turns it into class probabilities. For continuous targets, this entry uses ridge regression, a linear model with a penalty that keeps its coefficients from becoming too large.

Traditional MLNo regular feature limit specifiedSource ↗

Model profile

Logistic Regression

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	1,062	997–1,124	44
DNA methylation	1,113	1,045–1,174	4
GLM/PLM Embeddings	1,197	1,025–1,457	4
Gene expression	1,185	1,059–1,319	10
Genomic prediction	766	278–1,102	4
Metagenomics	1,134	1,065–1,217	10
Molecular properties	981	888–1,076	10
Other	723	340–898	2