How do you decide between model accuracy and model performance?
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Answer / Pravendra Singh Rathor
Model accuracy refers to how often the model makes correct predictions on a given dataset. Model performance, on the other hand, encompasses various aspects such as accuracy, speed, scalability, interpretability, and robustness. Deciding between them depends on the specific use case and goals of the project. For example, in some cases, a more complex model with high accuracy but poor speed might be acceptable if it significantly outperforms simpler models in terms of accuracy. Conversely, for real-time applications, speed might be more important than accuracy.
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