A data science team has developed a complex gradient boosting model to assess credit risk. To satisfy regulatory requirements, the team must provide a global explanation of the model's behavior. They need to visualize how the model's output changes, on average, as a single input feature varies across its range, while marginalizing the effects of all other features. Which post hoc explainability technique is specifically designed to generate this type of global insight?
Local SHAP (SHapley Additive exPlanations) value analysis
Variance Inflation Factor (VIF)
Partial Dependence Plot (PDP)
Local Interpretable Model-agnostic Explanations (LIME)
The correct answer is a Partial Dependence Plot (PDP). A PDP is a global model-agnostic method that shows the marginal effect of one or two features on the predicted outcome of a machine learning model. It visualizes the average prediction change as the feature's value changes, which directly addresses the scenario's requirement to explain the model's overall behavior to stakeholders.
Local Interpretable Model-agnostic Explanations (LIME) is incorrect because it is a local explanation technique. LIME explains why a model made a specific prediction for a single instance by creating a simpler, interpretable model in the local vicinity of that prediction.
Variance Inflation Factor (VIF) is incorrect as it is a diagnostic tool used during feature engineering to detect multicollinearity among predictor variables. It is not a post hoc technique for explaining a trained model's predictions.
Local SHAP (SHapley Additive exPlanations) value analysis is incorrect because, as specified, it is a local technique. While SHAP values can be aggregated to create global explanations, analyzing individual SHAP values explains the feature contributions for a single prediction, not the model's average behavior across the dataset.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a Partial Dependence Plot (PDP) and how does it work?
Open an interactive chat with Bash
How do PDPs differ from SHAP values in terms of explainability?
Open an interactive chat with Bash
What are the limitations of using a PDP for model explainability?