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Caught my AI model overfitting and it saved my project
I was getting great results in tests, but the model fell apart with new data. Now I split my data sets more carefully to avoid this trap. Has anyone else faced this?
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wren_perry5h ago
What if overfitting isn't the real problem here? Sometimes models learn too well because the data is just bad. Splitting sets might hide issues instead of fixing them. I've seen cases where clean data fixes overfitting better than any split. If your model falls apart with new data, maybe your features are wrong. Relying on splits can make you lazy about real data quality.
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adams.fiona4h ago
That bit about clean data fixing overfitting, @wren_perry, hits home. In my last project, we scrubbed the data spotless but skipped proper validation. The model aced training but totally bombed on new samples. So clean data helps, but you still need splits to catch those sneaky issues.
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milaj953h ago
Shocked to see splits being called lazy. Splits are a basic check in machine learning to see if models work on unseen data. I remember a project where we had clean data but no validation, and the model crashed when deployed. Clean data helps, but splits catch problems you can't see in training. Skipping them is like assuming your homework is right without checking answers. Honestly, I'm surprised anyone would argue against proper validation.
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