What checks reveal data quality issues hurting my model's performance?

[Sophia.L]

I’ve been trying to improve my model’s performance on a specific task, but I’m hitting a wall where adding more data or parameters isn’t helping. I’m starting to think the issue might be with the quality of my training data itself, but I’m not sure how to systematically diagnose what’s missing or misleading in the dataset.

[Edward_T]

Yeah, I went down that road too. We found a chunk of mislabeled examples in the training set, especially for edge cases. After cleaning those up, the model stopped getting worse with more data and the validation curve finally looked steadier.

[CamilaL]

Also learned that bigger data can help only if the new samples match what the model is actually supposed to learn. We added a few hundred thousand samples from a slightly different distribution and saw a dip on the holdout. It made me question whether the issue is data quality or just distribution mismatch.

[ScottH]

We did a quick, low-effort data audit: random checks, looked for label consistency across similar items, and tracked where the model’s mistakes clustered. Found a few systematic labeling biases and some features that never got covered in the data. The plan to fix those felt big and risky, so we parked it for now but it stuck in my head.

[Stephen.M]

I keep wondering whether the bottleneck is really the data or the training setup. I tried lots of small tweaks and no dramatic gains. Maybe the problem isn’t more data but a mismatch between the metric and what you actually want, or a leakage in the evaluation. Have you ruled out label leakage or metric alignment?