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Related Questions
- What are the potential risks of premature model convergence if training is stopped based on improvement rate rather than metric value?
- Can you explain how a declining improvement rate might indicate that the model has reached diminishing returns and is no longer improving on the validation metric?
- How can a fixed improvement rate be used to determine when to stop model training, considering the risk of overfitting and underfitting?
- What is the difference between a rapidly improving validation metric and a model that has plateaued, and how can we distinguish between the two?
- Can you provide examples of scenarios where stopping model training based on improvement rate has led to worse performance than continuing training past the threshold?
- How does the choice of improvement rate threshold impact the model's final performance, and are there any general guidelines for selecting an optimal threshold?
- Can you discuss the trade-offs between stopping model training based on improvement rate versus waiting for a significant improvement in the validation metric value?
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