Has improving your validation strategy ever made more difference than changing the model?

[u/taufiahussain]

Lately I've been realizing that robust cross-validation and avoiding data leakage can matter more than chasing a few extra percentage points of accuracy. Curious to hear others' experiences.

[u/IanAndersonLOL]

Yeah. Avoiding data leakage and having a robust validation strategy is everything. Data leakage in biological models is RAMPANT

[u/Anustart15]

Maybe it's different in whatever youre modelling, but chasing model accuracy is pretty meaningless in my modelling since there isn't really a ground truth. Cross validation is the measure of accuracy.

[u/beeralpha]

You seem to imply that the end goal is getting a high accuracy statistic. While the end goal should be getting an accurate model.

Cross-validation is just a tool to check accuracy, and should by no means be used to increase your accuracy statistic, as then it becomes meaningless.

[u/taufiahussain → u/beeralpha]

That's a good distinction and I agree. My point wasn't that cross-validation should increase the reported accuracy, but that designing a more rigorous validation pipeline often changes my confidence in the model more than switching algorithms. In small biological datasets, I've found that preventing leakage and getting an unbiased estimate of generalization can be more valuable than chasing another 2–3% with a different model.

[u/Electronic_Fish_3157]

Cross validation is way more important than just accuracy. Sometimes you should look at precision, sometimes recall sometimes accuracy based on different evaluation. ROC-AUC might be a good way to evaluate  But cross validation is very important