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| Metric | Value |
|---|---|
| Test accuracy | 58.71% |
| Test F1 score | 0.6225 |
| Hierarchical loss | 0.92111327 |
| P-adic loss (total) | 234.54004476 |
| P-adic loss (mean) | 0.25549024 |
| Prime base | 79 |
| Number of tags (input features) | 6,936 |
| Non-zero parameters | 2,696 / 2,441,824 (99.9% sparse) |
| L1 regularization (C) | 1.0000 |
| Training samples | 3,662 |
| Test samples | 918 |
| Agreement | Count | Share | Cost per mistake | Total contribution |
|---|---|---|---|---|
| Exact match | 542 | 59.04% | 0.000000 | 0.000000 |
| p^5 | 5 | 0.54% | 0.000000 | 0.000000 |
| p^4 | 10 | 1.09% | 0.000000 | 0.000000 |
| p^3 | 33 | 3.59% | 0.000002 | 0.000067 |
| p^2 | 52 | 5.66% | 0.000160 | 0.008332 |
| p^1 | 42 | 4.58% | 0.012658 | 0.531646 |
| p^0 | 234 | 25.49% | 1.000000 | 234.000000 |
L1 (Lasso) regularization promotes sparsity by driving many coefficients to exactly zero. This model uses ALL available tags (6,936) but L1 regularization selects which features are actually used. The number of non-zero parameters (2,696) indicates how many coefficients the model actually uses.