InterpretationOverall

interpretationOverall is a term used in discussions of model interpretability to describe an aggregate view of how well a model's decisions can be understood. It refers to a summarized assessment that combines both global explanations about overall feature importance and local explanations for individual predictions.

In practice, interpretationOverall can be represented as a score, report, or set of metrics that reflect interpretability across the model’s outputs. It captures how transparent the model is to stakeholders and how easily its behavior can be traced to input features or rules. Global explanations identify which features drive general decisions, while local explanations explain why a specific prediction was made.

Computing interpretationOverall often involves aggregating explanations from established methods such as SHAP values, permutation feature importance, and surrogate models. Common approaches include averaging or ranking local explanations, evaluating the consistency of explanations across similar instances, and assessing the coverage of meaningful explanations for critical decisions. The goal is to produce a coherent summary that communicates which factors matter most and how reliably those factors account for predictions.

InterpretationOverall is influenced by data quality, model complexity, feature correlation, and the intended audience. A high interpretationOverall does not guarantee model accuracy; it indicates that the model’s reasoning is more readily explained. In practice, teams use interpretationOverall to support trust, governance, debugging, and regulatory compliance, while recognizing its context-dependent nature and complementing it with domain-specific explanations.

See also model interpretability, explainable AI, SHAP, LIME.