What’s the best way to diagnose label noise in ML models?

[JeffreyLH]

I’ve been trying to improve my model’s performance on a specific task, but I’m worried my training data might have some hidden label noise that’s causing it to learn the wrong patterns. How do you even start to diagnose something like that without a perfectly clean validation set?

[Hannah27]

I've chased this one before. I started with a label audit: pull a few hundred samples at random, double-check them with a human, and map where the disagreements cluster. If you see a pattern—certain classes, certain annotators—that's a clue about label noise (or an annotation protocol issue).

[Isabella.W]

Then I tried measuring robustness: train with label noise aware loss on a tiny subset and watch how much the validation metric moves when I flip labeled examples. The changes were small for some tasks and wild for others, which was scary and enlightening.

[Chloe.J]

Could it be that the real issue is not noisy labels but a mismatch between train and test distribution, or an ill-defined task?

[Jack_B]

Another moment: I dropped a chunk of data from a noisy annotator and watched how the model behaved on a different subset. It shifted the predictions in the right direction sometimes, sometimes not. Made me pause about removing data vs cleaning it.