In this episode, Lucas and Luna dive into conformal prediction, a model-agnostic framework that gives machine learning models reliable uncertainty estimates without sacrificing coverage guarantees. They discuss how it works — using a calibration set to produce prediction sets with a user-specified confidence level — and walk through a concrete example from medical imaging where a model flags skin lesions. They contrast it with Bayesian methods and softmax probabilities, and explore why it's gaining traction in regulated industries like healthcare and finance. No prior knowledge of conformal prediction required; just a curious mind about making AI more trustworthy. If today's tech conversation gave you something usable, consider supporting the show at buy me a coffee dot com slash fexingo — keeping it free from ads so we can focus on substance.

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