Caught my ML model memorizing training data instead of learning patterns

I was working on a sentiment analysis tool for my company's customer reviews, and after 3 months of tweaking parameters, I noticed it was scoring 99% accuracy on the test set. Turned out I forgot to shuffle the data before splitting, so it just learned the order of responses instead of actual language patterns. Has anyone else stumbled into this trap with their own datasets?

2 comments

2 Comments

avery_fox931mo ago

Oh yeah, the old "forgot to shuffle" mistake. I did something similar a few years back with a customer churn model. The fix that really helped me was setting up a proper cross-validation pipeline with stratified folds. That way even if I mess up the initial split, the model gets tested on a balanced mix each time. I also started logging the seed values for every random operation so I could trace back through my steps when something felt off.

wendy_murphy161mo ago

Wait, you really spent 3 months tweaking parameters before noticing it was just memorizing the data order? That's wild to me, I would have caught that in like a week tops when the loss curve looked too perfect. Honestly I'm impressed you managed to get 99% accuracy from memorizing sequence patterns without realizing something was off sooner. The whole thing is a perfect reminder that blind faith in high scores is dangerous. At least you figured it out before deploying, some people never notice and just ship broken models.

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