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The Transparent Explanations Algorithm (TEA) provides the ability to generate top-n influential variables with a rank for each prediction of a machine learning model. It is a generic capability intended to be used for any machine learning model, black-box or otherwise. Originally conceptualized with the intent to explain outcomes of credit models for purposes of adverse action, it can also function as a robust model diagnostic solution for nearly any predictive model.