MediAnalytics built an XGBoost classifier for hospital readmission predictions and used SageMaker Clarify for post-training explainability. Global SHAP summary shows patient_age as a top positive contributor to readmission risk, but the Clarify-generated partial dependence plot (PDP) for patient_age is non-monotonic and appears to conflict with SHAP. The data team suspects patient_age is correlated with several comorbidity features. What is the most appropriate interpretation and next step?

The correct answer recognizes that Partial Dependence Plots (PDPs) assume feature independence and can be misleading when features are correlated, as is suspected with patientage and comorbidity features. SHAP values, while robust, can also be influenced by correlations when interpreting individual feature contributions in isolation. To resolve the apparent conflict and understand the true relationship, it's crucial to investigate feature interactions and correlations more deeply. SHAP dependence plots, Individual Conditional Expectation (ICE) plots, or conditional PDPs can provide a more accurate view of how patientage affects predictions, especially when considering its relationship with other features. This allows for an informed decision before model deployment. Incorrect options: Trusting PDP over SHAP is incorrect because PDP's independence assumption makes it unreliable in this scenario. Removing patientage is premature and could lead to a less accurate or interpretable model without further investigation. Switching to LIME is not necessarily better; LIME also has limitations with correlated features and doesn't directly address the underlying issue of understanding feature interactions in this context.