@petegaskell

Machine learning models risk data leakage when future information contaminates training. To prevent this, strict chronological splits must be enforced, with training only on past data. Cross-validation should use rolling or expanding windows, not random shuffles. Feature sets must avoid lookahead bias—e.g., excluding post-event variables. Robust pipelines also test models on truly unseen regimes. Avoiding leakage ensures reported accuracy translates into live performance. Ultimately, leakage control is the difference between robust predictive power and misleading backtest overfitting.

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