mechanistic
Analysis v1
If a machine learning model has more features (like traits or measurements) than data points (like people or samples), it can 'cheat' by finding fake patterns in random noise, especially when tested the wrong way — making it look better than it really is.
Evidence from Studies
Supporting (1)
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Machine learning algorithm validation with a limited sample size
Computational/Algorithm Study
2019The study shows that when you have more features than samples, especially in small studies, regular cross-validation can trick you into thinking your model works better than it really does. Using better testing methods fixes this problem.
Contradicting (0)
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No contradicting evidence found
Gold Standard Evidence Needed
According to GRADE and EBM methodology, here is what ideal scientific evidence would look like to definitively prove or disprove this specific claim, ordered from strongest to weakest evidence.