Answer / Sarita Chaudhary

In many machine learning scenarios, both model accuracy and model performance are crucial. Model accuracy refers to the ability of a model to make correct predictions on unseen data, while model performance encompasses other metrics such as speed, scalability, and generalizability. Ideally, a model should strive for high accuracy AND good performance. However, in certain situations, one may be more important than the other. For example, in safety-critical applications, model accuracy might be prioritized over performance, while in real-time systems or large-scale deployments, performance could take precedence.

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