How to read this board

Avg rank

Algorithms are ranked 1st, 2nd, 3rd… on every dataset, then those ranks are averaged. Raw metric values are never averaged across datasets — scales differ.

Wins

Datasets where the algorithm placed first.

Rank per dataset

One cell per dataset (not per time window): teal = ranked 1st on that dataset, clay = ranked last. A solid-teal strip is consistent everywhere; scattered clay shows exactly where it loses. Hover a cell for the dataset name and rank.

Log-loss

The classification ranking metric: mean negative log-probability given to the true label (lower is better, 0 is perfectly confident and correct). A proper scoring rule over predicted probabilities — unlike accuracy-family metrics, it cannot be gamed by echoing recent labels on autocorrelated streams.

AUC (binary)

ROC-AUC over the full stream of predicted scores, shown for binary classification datasets: threshold-free and robust to class imbalance.

κt

Kappa-temporal: skill over the predict-the-previous-label baseline. 100 perfect, 0 no better, negative worse. Display-only; shown as a median — it is unbounded below.

κ

Cohen's kappa: chance-corrected accuracy, robust to class imbalance — the number to trust on skewed streams (imbalanced datasets are flagged in the catalog).

R²

Regression fit, scale-free across datasets: 1 is perfect, 0 is no better than predicting the mean.

ARI

Clustering agreement with the ground-truth labels, scored over evaluation windows: 1 is perfect, 0 is random grouping.

AUC

Anomaly detection ROC-AUC over the full stream of scores: 1 is perfect, 0.5 is random.

Wallclock

Total compute across all datasets and seeds. Read it against rank — in streaming, a small quality gap for a 100× time gap is the whole story.