PCNN Fold 0

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Fold metrics

Metric	Value
Test accuracy	13.26%
Test F1 score	0.1083
Hierarchical loss	0.78060260
P-adic loss (total)	1250.46660000
P-adic loss (mean)	0.69663876
Prime base	79
Hidden layer size	27
Max tags	32
Training samples	7,315
Test samples	1,795

P-adic loss breakdown

Agreement	Count	Share	Cost per mistake	Total contribution
Exact match	238	13.26%	0.000000	0.000000
p^3	48	2.67%	0.000002	0.000097
p^2	146	8.13%	0.000160	0.023394
p^1	114	6.35%	0.012658	1.443038
p^0	1,249	69.58%	1.000000	1249.000000

Tag Rank vs First-Layer Weight Magnitude

Tag rank vs max first-layer weight magnitude — Scatter plot showing the relationship between tag battle ranking and maximum absolute first-layer weight value across all hidden units. Shows which input features contribute most to the parameter constrained neural network's hidden representations.

About p-adic loss

P-adic loss measures the distance between predicted and true taxonomy using p-adic metric (base 79). Lower values indicate closer predictions in the taxonomy hierarchy. This metric is shared with the umllr model for comparison.