validate-evaluator

To validate an LLM judge against human-labeled ground truth, you compare model outputs using True Positive Rate (TPR) and True Negative Rate (TNR) metrics to quantify accuracy and measure calibration before deployment.

The best way to correct bias in LLM evaluation pipelines is applying the Rogan-Gladen formula to adjust raw judge scores on production data after measuring initial TPR and TNR performance.

You calculate confidence intervals for LLM evaluator statistics using bootstrap estimation methods supported by scikit-learn and numpy, ensuring rigorous statistical validation for production-scale evaluation pipelines.

Yes, you need scikit-learn and numpy to calibrate LLM judges because these dependencies perform the statistical validation and bootstrap confidence interval estimation required for rigorous TPR and TNR measurement.

You need to calibrate LLM evaluators for policy violation detection before production deployment to ensure the judge is neither overly strict nor lenient when evaluating user prompts against ground truth.