Two-Stage Workflow Helps Developers Catch Data Leakage Before Model Deployment

Data leakage is a common but often overlooked problem in machine learning, where information from outside the training set artificially inflates model accuracy during development but causes failure in production. A developer has proposed a standardized two-stage workflow to pinpoint the exact columns responsible for leakage before any hyperparameter tuning begins. The first stage uses Pearson correlation and single-feature R² scores to flag any feature that linearly explains more than 10% of the target variable's variance on its own. The second stage employs a shallow decision tree to detect non-linear leakage patterns that linear correlation alone would miss, such as categorical IDs that cleanly separate target classes. Together, the two checks are designed to be computationally fast and actionable, giving practitioners a clear signal about where leakage originates rather than just confirming that it exists.