In iterative training algorithms, what diagnostic observation signals the onset of overfitting due to prolonged training?
Answer
The validation error starts to increase after having peaked, even as the training error continues to decrease or remain near zero.
The divergence in error metrics is key: continued training past the optimal point dedicates learning resources to noise embedded in the training batches, causing the performance on unseen data (validation error) to worsen.
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