interpretable machine learning
Interpretable machine learning refers to the ability to understand and explain the predictions or decisions made by a machine learning model in a way that humans can comprehend. It involves algorithms and techniques that prioritize transparency and provide meaningful insights into how the model reached its conclusions, helping to build trust and enhance human understanding of the model's behavior.
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