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Human-in-the-loop Agentic ML audit system for tabular datasets — detects data risks, possible leakage, class imbalance, recommends metrics, benchmarks baseline models, tracks experiments, and generates grounded audit reports.
Pre-training feature leakage auditor for tabular ML datasets. Checks column names, target correlation, categorical proxies, future timestamps, ID columns, and train/test distribution shift before any model is trained.