Model abstraction simplifies complex systems by focusing on essential features while hiding unnecessary details. It is used in software engineering, data science, and machine learning to improve efficiency, scalability, and interpretability. Developers, researchers, and business analysts benefit from clearer insights, faster prototyping, and reduced cognitive load when managing intricate models or datasets.
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