Interpretability refers to the degree to which a human can understand the reasons behind a model's predictions or decisions. In the context of machine learning and artificial intelligence, interpretability is crucial because it allows users to comprehend how models arrive at their conclusions, which is important for trust, transparency, and accountability. There are several key aspects to interpretability: 1. **Transparency**: A model is considered interpretable if its inner workings are clear and can be easily understood.
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