Build a leakage-safe benchmark

Design a benchmark to test whether synthetic fraud transactions can train a useful model without leaking test information. Split by leakage unit first; fit preprocessing and synthesis only on train; evaluate once on untouched real test data. The expensive mistake is fitting encoders, rare-category grouping, or the synthesizer on the full dataset before splitting. That can leak test-set structure even without exact row copies. Step 1 Split real data by merchant_id into train, validation, and test before any preprocessing. Merchant-level grouping prevents the model from seeing synthetic versions of merchants later used for test. Step 2 Fit imputation, category bucketing,…

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