Conformal intervals under a log transform: the blow-up isn't a back-transform bug
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Someone log-transforms their target, fits a model, wraps it in conformal prediction, inverse-transforms the intervals back with expm1 , and the upper bound comes out at 350x the point forecast . The natural reaction is "the back-transform is broken." I ran into exactly this framing on a real bug report recently, and the interesting part is that the back-transform is correct — the interval is doing precisely what it should. What looks like a bug is two honest effects stacking. Here's the mental…
1Key Takeaways
- Someone log-transforms their target, fits a model, wraps it in conformal prediction, inverse-transforms the intervals back with expm1 , and the upper bound comes out at 350x the point forecast .
- The natural reaction is "the back-transform is broken." I ran into exactly this framing on a real bug report recently, and the interesting part is that the back-transform is correct — the interval is doing precisely what it should.
- What looks like a bug is two honest effects stacking.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that someone log-transforms their target, fits a model, wraps it in conformal prediction, inverse-transforms the intervals back with expm1 , and the upper bound comes out at 350x the point forecast .
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