How can I tell if my validation split actually represents real-world data?

[Savannah_G]

I’ve been training a model on the same dataset for a while and the validation accuracy looks solid, but I’m starting to question whether my validation split actually represents the kind of data the model will see in the real world. It feels like the metrics might be flattering because the split captures dataset quirks rather than true generalization, and I’m not sure how to sanity-check that assumption.

[RyanQJ]

I’m also uneasy because most of the validation data comes from the same collection process as the training set. Even though the split is clean on paper, I’m not convinced it reflects how the data distribution behaves once the model is exposed to newer or slightly different sources.

[OliverS]

I did something similar last quarter and felt the same. Accuracy looked good but I found cues that only showed up when I analyzed by row source. It smelled like memorizing quirks.

[FrankT]

I switched to a realization that the split was not representing real data because we had a distribution shift between months. When I tested with a holdout from a different month the score dropped.

[Mila3]

We tried stratified sampling and also a smaller test from a different field, and the scores drifted apart. It helped to see which categories were leaking.

[Kyle93]

I wanted to do cross validation but it became heavy with our model size. In the end I ran a few folds on a subset and observed more variance.

[MiaUD]

Do you have any sense that the data practice or labeling changed over time?

[Mason.M]

The problem might not be the split at all but the labeling guidelines changing with the data. I chased that a bit and it was a dead end.

[Chloe57]

I sometimes keep a tiny random sample from the training data aside and check if the model uses it. If accuracy changes a lot then something is wrong.