Shapley Additive Explanations (SHAP) is a game-theoretic approach to decipher machine learning models. It aids in understanding individual model predictions by attributing each feature's importance. Data scientists and analysts significantly benefit from this robust method. They employ it to improve model transparency, enabling trustworthy and fair decision-making across all industries.
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