Exam Certified Machine Learning Professional topic 1 question 21 discussion

C is partially correct. However, going with A since it contains the method itself. Using mlflow.shap.log_explanation, you can automatically calculate and log Shapley feature importance plots, providing insights into the importance of different features in the model's predictions.

Answer A. https://mlflow.org/docs/latest/python_api/mlflow.shap.html#mlflow.shap.log_explanation "... computes and logs explanations of an ML model’s output. Explanations are logged as a directory of artifacts containing the following items generated by SHAP (SHapley Additive exPlanations). - Base values - SHAP values (computed using shap.KernelExplainer) - Summary bar plot (shows the average impact of each feature on model output)"

mlflow.shap: Automatically calculates and logs Shapley feature importance plots. # Generate and log SHAP plot for first 5 records mlflow.shap.log_explanation(rf.predict, X_train[:5])

C. mlflow.shap