A data science team at a credit union has deployed a high-performance, but complex, XGBoost model for loan default prediction. To comply with financial regulations and improve customer trust, the team must provide a specific reason for each individual loan denial. The explanation must quantify the positive or negative impact of each applicant feature (e.g., credit score, income, loan amount) on the final decision for that specific applicant. Which of the following methods is most suitable for generating these explanations?
Global feature importance using permutation, as it ranks the most influential features for the model's overall predictions.
SHAP (SHapley Additive exPlanations), because it computes the contribution of each feature to a specific prediction, providing a theoretically sound way to quantify individual feature impacts.
Principal Component Analysis (PCA), because it can reduce the feature space to the components that explain the most variance in the data.
LIME (Local Interpretable Model-agnostic Explanations), because it can approximate any black-box model with a local, interpretable model to explain a single prediction.
The correct answer is SHAP (SHapley Additive exPlanations). The scenario requires quantifying the impact of each feature for an individual prediction, which is the core strength of SHAP. It uses concepts from cooperative game theory to fairly distribute the prediction outcome among the features, providing precise, additive explanations (SHAP values) for a single instance. This makes it ideal for regulatory contexts where a clear accounting of feature contributions is necessary.
LIME (Local Interpretable Model-agnostic Explanations) is incorrect because while it provides local explanations, it does so by creating a new, simpler model (e.g., a linear model) that approximates the complex model's behavior around a single point. This approximation may not be as faithful or consistent as SHAP values in quantifying the exact contribution of each feature, making it less suitable for the stated requirement of precise quantification.
Global feature importance is incorrect because it explains which features are most important to the model on average across the entire dataset. It cannot provide an explanation for an individual prediction, which is the specific requirement of the scenario.
Principal Component Analysis (PCA) is incorrect because it is a dimensionality reduction technique used during data preprocessing. Its purpose is to transform the original features into a new set of uncorrelated components, not to explain the predictions of a trained model.
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Why is global feature importance insufficient for individual predictions?