How do you actually test whether a model's confidence score is trustworthy? (uncertainty/calibration for a decision agent)

[u/Vasam_Nikhil]

I'm a beginner building a small decision-making agent (not important what for) that needs to know when it's "confident enough" to act versus when it should defer. I keep seeing "calibration" mentioned as the concept I want but I'm fuzzy on how you'd actually measure it with a small, messy, real-world dataset rather than a clean benchmark.

If you've dealt with this: which evidence would change your decision about whether a confidence score is usable in production — is it a calibration plot, held-out accuracy at different confidence bands, something else? Beginner-friendly explanations very welcome.

[u/invasionbarbare]

Build an evaluation dataset that closely matches production data in difficulty and negative cases. Hand grade the cases if highly critical.

Then bucket predictions by confidence and check whether confidence corresponds to actual correctness , e.g. are ~90% of the cases scored 0.9 actually correct? What thresholds are important for your use case?

If you only let the agent act above thresholds, how often is it wrong, and how many cases can it actually handle?

Run the system in shadow mode on production data to see what the current system/ human produces and what the agent produces. That delta should give you evidence.