A financial services company has deployed a high-performance, complex gradient boosting model for real-time credit risk assessment. While the model demonstrates superior accuracy, regulatory requirements mandate that the company must provide a clear, human-understandable justification for each individual loan application that is denied. The data science team is tasked with implementing a method to explain the specific factors leading to each denial decision generated by their existing 'black box' model. Which of the following approaches is MOST appropriate for this specific requirement?
The correct answer is local explanations. The scenario requires an explanation for each individual loan denial, which is the definition of a local explanation. Local explanation methods, such as LIME (Local Interpretable Model-agnostic Explanations) or instance-specific SHAP (SHapley Additive exPlanations) values, are designed to interpret a single prediction made by a complex model, showing how different feature values for that specific instance contributed to the outcome.
Global explanations describe the overall behavior of the model across the entire dataset, such as which features are most important on average, but do not explain the reasoning for a single, specific prediction.
Interpretable models are models that are understandable by design, such as linear regression or simple decision trees. While they would meet the transparency requirement, the task is to explain the existing complex model (post hoc), not to replace it with a new, inherently interpretable one.
Model drift analysis is the process of monitoring changes in the model's performance or data distributions over time. It addresses whether the model is still relevant, not why it made a particular prediction at a specific point in time.
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What are local explanations, and how do they work?
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How are local explanations different from global explanations?
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Why wouldn't replacing the black-box model with an interpretable model be suitable in this case?