# Claim 6: Hyperparameter sensitivity (lambda_p)


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**Verdict: TOY-VERIFIED**

Script: `lambda_p_sweep.py`. 12 reps, `hierarchical_shrinkage=True`, λs=25/λn=50 fixed, λp swept
over the paper's own values, accuracy + FP/FN reoccurrence at N=50 and N=100.

| λp | Accuracy | FP reocc. | FN reocc. |
|---|---|---|---|
| 0 | 96.92% | 6.65% | 0.77% |
| 0.5 | 96.99% | 6.55% | 0.79% |
| 1.0 | 97.03% | 6.55% | 0.76% |
| 5.0 | **98.28%** (peak) | 2.84% | **0.16%** (best) |
| 10.0 | 97.84% | 2.66% | 0.71% |
| 20.0 | 97.80% | 2.66% | 0.92% |
| 50.0 | 97.26% | **2.31%** (best) | 1.56% |

Paper: "higher λp can benefit accuracy, productive monotonicity, and error reoccurrence. λp=25.0
retains these benefits while maintaining high precision near 100%." Direction matches cleanly:
moving from λp=0 to any λp≥5 roughly halves FP reoccurrence and substantially improves accuracy,
with a slight FN uptick only at the largest value tested (λp=50). This is consistent with the
paper's own framing of λp=25 as a middle-ground choice rather than a monotonic "more is always
better" relationship. λp=25 itself was not one of the paper's own swept values ({0, 0.5, 1, 5, 10,
20, 50}), so no direct single-point comparison is possible — matches the paper's own sweep grid.