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David
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DavidBegginer
Asked: May 29, 20252025-05-29T21:36:40+00:00 2025-05-29T21:36:40+00:00In: Machine Learning

I trained my model, but it's performing too well on validation — could this be data leakage? How do I check for that?

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I’m seeing 98–99% accuracy on my validation set, but when I test on truly unseen data, the performance drops significantly. Suspecting some leakage but not sure where it’s happening

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  1. joseph1
    joseph1 Begginer
    2025-05-29T21:49:27+00:00Added an answer on May 29, 2025 at 9:49 pm

    I once trained a model that was performing way too well on the validation set — like, suspiciously good. At first, I was excited… but something felt off. Turned out, it was data leakage.
    Here’s what I did to figure it out:

    • I rechecked my data splits and found that some similar entries had ended up in both training and validation.
    • I reviewed my features — one of them was indirectly revealing the target.
    • I even tested a basic model, and it still performed too well, which confirmed my suspicion.

    Lesson learned: if your model feels like it’s “too perfect,” always check for leakage. It’ll save you a ton of headaches later. Adopt this, It may solve this problem.

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  2. Hassaan Arif
    Hassaan Arif Enlightened
    2025-05-29T22:46:55+00:00Added an answer on May 29, 2025 at 10:46 pm

    Absolutely, I’ve been in that spot, getting 98 to 99 percent accuracy on validation and feeling confident, only to see the performance drop a lot on truly unseen data. That’s usually a sign of data leakage. What helped me was carefully checking my data splits to make sure training and validation sets didn’t overlap. I also reviewed my features to find anything that might accidentally reveal the target. Sometimes a feature acts like a shortcut without you realizing it. I looked for very high correlations between features and the label because if something is almost perfectly correlated, that’s suspicious.

    Finally, I tried a simple model. If it also performed too well, it was another clue leakage was happening. Fixing these things usually made validation accuracy drop, but then the results matched real-world performance better, which is what really matters.

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