MLflow provides a module called mlflow.shap that can be used to automatically calculate and log a Shapley feature importance plot using the SHAP library. The mlflow.shap.log_explanation function takes a predict function and a sample of input data, and computes the SHAP values and base values for each feature. It then logs the following artifacts to the current run:

Base values

SHAP values (computed using shap.KernelExplainer)

Summary bar plot (shows the average impact of each feature on model output) The mlflow.shap.log_explanation function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc1234 References:

mlflow.shap — MLflow 2.9.1 documentation

Extract feature importance from a mlflow 1.9 PyFuncModel model

9.6 SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning

load MLFlow model and plot feature importance with feature names