Comments (3)
This is great! Thanks so much for doing all this work.
Let me try and digest it a little bit before commenting.
The point of naive
is to be totally naive --- as if we ran conformal assuming there was no distribution shift.
from conformal-prediction.
Sure no worries - in case it is helpful, I uploaded the adaptation to your notebook documenting the experiments I referenced above https://gist.github.com/orisenbazuru/72236a74083e48db06daf838b5def0e6
from conformal-prediction.
Beautiful, thanks :)
from conformal-prediction.
Related Issues (8)
- Score function for APS HOT 1
- [Question] Why the upper bound of the selective risk is non-monotonic in the tutorial of selective classification? HOT 4
- Conformal risk control question HOT 5
- `np.quantile`: deprecated `interpolation` argument HOT 3
- colab weather example won't run HOT 4
- Predictive uncertainty in weather-time-series-distribution-shift notebook HOT 2
- Size of Prediction Sets using APS Different Than Reported in RAPS Paper HOT 5
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from conformal-prediction.