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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_perry7mo 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.fiona7mo 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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milaj957mo 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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davis.xena7mo ago
Ever tried making your features simpler? I once had a model that memorized date patterns instead of learning the real trend. I stripped out the fancy date parts (like day-of-week) and just used the raw number, and suddenly it worked on new data. Sometimes the model is just using a cheat code you built into the data itself. A split shows you the problem, but cleaning up what you ask it to learn fixes it.
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