#CurrencyPairPrediction Using SHAP Values to Explain Model Decisions SHAP (SHapley Additive exPlanations) values provide a powerful method to explain the output of machine learning models by quantifying each feature's contribution to a specific prediction: Feature Attribution: SHAP shows how much each input feature pushed a prediction higher or lower, helping users understand model reasoning. Consistent and Local Explanations: It offers explanations for individual predictions (local) while maintaining fairness and consistency across the dataset (global). Model-Agnostic: SHAP can be applied to any machine learning model, including complex ones like gradient boosting and neural networks. Improved Trust and Transparency: In finance, SHAP helps traders, analysts, and regulators interpret decisions, improving trust in AI-driven strategies. SHAP enhances model interpretability by making predictions more understandable, auditable, and actionable.