Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach for explaining the output of any machine learning model.
SHAP (SHapley Additive exPlanations) is a game theoretic approach. SHAP considers marginal contribution for every feature, to explain the output of any machine learning model. For a given sample data set, SHAP permutes the values for each feature, to see how much the particular feature is affecting your output, and that marginal contribution will be your estimation for that particular feature.
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