If you're testing a machine learning model using a common method called K-fold cross-validation, you might think it's working better than it really is—especially if you're tuning the model using all your data first. This can trick you into believing your model is accurate when it won't work as well on new data.

Evidence from Studies

Supporting (1)

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Machine learning algorithm validation with a limited sample size

Computational/Algorithm Study

2019

The study shows that a common method for testing AI models can make them look better than they really are, especially when there isn’t much data, which is exactly what the claim says.

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Source Study

Machine learning algorithm validation with a limited sample size

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DOI: 10.1371/journal.pone.0224365

Similar Assertions

Nested cross-validation is like having two layers of checkups when testing a model—it keeps the test data totally separate so the model doesn’t cheat, giving a fairer score no matter how much data you have.

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Splitting data into training and testing sets gives a fair measure of how well a machine learning model works—just as reliable as more complex methods—because it keeps the test data completely separate so the model doesn't cheat by seeing it early.

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72%