Model interpretability refers to the degree to which a human can understand the reasons behind a model's decisions and predictions. It emphasizes the transparency and comprehensibility of a model, allowing users to grasp how features contribute to outcomes, which is essential for trust, accountability, and validation of models in practice.
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