A focused independent review of one supervised ML evaluation, from the unit of independence and feature timing to metric choice, uncertainty, and reporting limits. I trace the evaluation boundary and show which claims are supported by the supplied pipeline and artifacts.

Scope: one classification or regression target and one primary evaluation pipeline; up to two split strategies; up to three candidate or baseline models; entity, group, site, session, duplicate, and time-boundary risks as relevant; preprocessing, feature construction, resampling, label, external-data, and tuning leakage paths; metric fit, thresholds, baselines, subgroup reporting, and uncertainty.

You receive an evaluation-boundary map, evidence-backed leakage register, review of splits and preprocessing isolation, a concise results-and-limitations rewrite, a prioritized remediation plan, and one 30-minute findings call or equivalent written Q&A. Delivery is within 6 business days after complete inputs. One factual-correction or clarification round is included.

This audit does not include model development, feature engineering, hyperparameter search, production monitoring, fairness certification, security testing, formal privacy review, clinical or regulatory validation, or a guarantee that every possible leakage path is absent. De-identified prediction exports, schemas, synthetic fixtures, and aggregate tables are preferred. Do not send credentials, direct identifiers, secrets, or data you are not authorized to share.