Training loss decreases smoothly.
Validation loss fluctuates.
Regularization is enabled.
Still, generalization is poor.
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Training loss decreases smoothly.
Validation loss fluctuates.
Regularization is enabled.
Still, generalization is poor.
Overfitting can persist if data leakage or feature shortcuts exist. Check whether features unintentionally encode target information or future data. Regularization can’t fix fundamentally flawed signals.
Also examine whether validation data truly represents unseen scenarios. Common mistakes include: Trusting regularization blindly, Ignoring feature leakage, Using weak validation splits
The takeaway is that overfitting is often a data problem, not a model one.