External validation is often presented as the gold standard for proving that a predictive model works beyond its original dataset. It is supposed to show that the model can generalize to the real world. But what if one external dataset is still far too small a test of the outside world?
In this episode, we break down why external validation often overpromises, how “different” datasets can still be too similar, and why transportability is a much harder claim than validation language suggests.
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