Latest Benchmark

Rolling nightly benchmark compiled from the live operational runs. Snapshot label: latest-2026-07-13T2043Z.

Trained params is the average non-zero parameter count in the fitted model across folds. Avg active params / classification is the mean number of active parameters or scoring decisions touched while classifying one product.

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Benchmark dashboard chart for latest benchmark view — Small-multiples dashboard generated directly from the comparison table rows below.

Model comparison chart for latest benchmark view — Log-log scatter of trained parameters versus mean p-adic loss, overlaid with the current parsimoniousness baseline.

Active parameter comparison chart for latest benchmark view — Scatter plot of avg active params versus mean p-adic loss, excluding PCLR and PCNN. Fitted equation: mean p-adic loss = -0.1180 log10(active params) + 0.4876. Regression fitted on log10(active params): R²=0.416, p=0.118.

Active parameter versus log-loss chart for latest benchmark view — Scatter plot of avg active params versus log10(mean p-adic loss), excluding PCLR and PCNN. Fitted equation: log10(mean p-adic loss) = -0.1539 log10(active params) - 0.3912. Regression fitted on log10(active params): R²=0.598, p=0.041.

Model	Trained params	Mean p-adic loss	Exact acc.	Prefix-2 acc.	Avg active params / classification
Dummy Baseline	1.0	0.918474	3.53%	1.44%	1.00
Importance-Optimised p-adic Linear Regression	552.0	0.293604	57.72%	60.54%	0.95
Parameter-constrained Logistic Regression	13596.0	0.731273	9.44%	18.30%	610.26
Unconstrained Logistic Regression with L1	3562.6	0.123097	69.81%	76.57%	423.39
Parameter-constrained Neural Network	12427.0	0.712654	17.20%	24.36%	11575.99
Decision Tree	25546.8	0.135724	65.49%	75.70%	232.37
Level-wise Logistic Regression	124338.6	0.148589	60.37%	72.99%	233.91
Unconstrained Neural Network with L1	33364.0	0.170735	66.18%	72.05%	5429.43
Zubarev (greedy init.)	605.0	0.308886	53.74%	59.05%	1.15