How do you handle coverage guarantees from split conformal prediction when the data shifts? [D]
Split conformal prediction gives you a clean statement: with a calibration set exchangeable with future data, your prediction sets cover the true label at the rate you chose.
Proof1 community thread
Key points
- The trouble is that production data rarely stays exchangeable.
- Categories get added, ticket wording changes, and a calibration set from three months ago quietly stops being representative.
- Do you recalibrate on a schedule, monitor empirical coverage and alert when it drops, or use an adaptive variant?
- And how do you decide what to do with the cases where the set contains several labels, such as route them to a human, an LLM, or a fallback rule?
Sources (1)
- [1]How do you handle coverage guarantees from split conformal prediction when the data shifts? [D]r/MachineLearning (top, daily) · Oct 13, 10:05 AM
Split conformal prediction gives you a clean statement: with a calibration set exchangeable with future data, your prediction sets cover the true label at the rate you chose.
The trouble is that production data rarely stays exchangeable.
Extractive summary: sentences quoted from the sources.
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