Cross-validation is one of the most common tools in machine learning.
It is supposed to give you a reliable estimate of how your model will perform.

But what if that estimate is quietly misleading you?

In this episode, we break down why cross-validation often fails in real-world healthcare and public health data. From data leakage and time dependence to population shifts and deployment mismatch, you will learn why validation strategies that look rigorous can still produce fragile models.

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