Interpretability refers to the degree to which a human can understand the reasoning behind a model's predictions or decisions. It plays a crucial role in ensuring that complex models can be communicated clearly, making it easier for users to trust and validate the results. In the context of data visualization, interpretability aids in translating high-dimensional data into understandable formats, which is essential for effective decision-making.
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