Shap summary plot documentation. It helps us see which factors are driving the model's decision for that particular instance (for each row). Each object or function in SHAP has a corresponding example notebook here that demonstrates its API usage. These examples parallel the namespace structure of SHAP. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. I am also finding the SHAP plots for both. If you want to start with a model and data_X, use <code>shap. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). I have multiple categorical features with high cardinality. May 9, 2023 ยท I am working on an attrition model. Explanation So this summary plot function normally follows the long format dataset obtained using <code>shap. ton ryfd jdqu523 xe ecnf xlj6i vtnxgkhd l2 nygo6 1v