# Validation: Calibration (Python)
Calibrate LLM judges against human labels. Target >80% TPR/TNR/Accuracy.
## Calculate Metrics
```python
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(human_labels, evaluator_predictions))
cm = confusion_matrix(human_labels, evaluator_predictions)
tn, fp, fn, tp = cm.ravel()
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)
print(f"TPR: {tpr:.2f}, TNR: {tnr:.2f}")
```
## Correct Production Estimates
```python
def correct_estimate(observed, tpr, tnr):
"""Adjust observed pass rate using known TPR/TNR."""
return (observed - (1 - tnr)) / (tpr - (1 - tnr))
```
## Find Misclassified
```python
# False Positives: Evaluator pass, human fail
fp_mask = (evaluator_predictions == 1) & (human_labels == 0)
false_positives = dataset[fp_mask]
# False Negatives: Evaluator fail, human pass
fn_mask = (evaluator_predictions == 0) & (human_labels == 1)
false_negatives = dataset[fn_mask]
```
## Red Flags
- TPR or TNR < 70%
- Large gap between TPR and TNR
- Kappa < 0.6
