Conditional validity of inductive conformal predictors
About
Conformal predictors are set predictors that are automatically valid in the sense of having coverage probability equal to or exceeding a given confidence level. Inductive conformal predictors are a computationally efficient version of conformal predictors satisfying the same property of validity. However, inductive conformal predictors have been only known to control unconditional coverage probability. This paper explores various versions of conditional validity and various ways to achieve them using inductive conformal predictors and their modifications.
Vladimir Vovk• 2012
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Conformal Prediction | ImageNet | Average Prediction Set Size4.2 | 63 | |
| Conformal Prediction | CIFAR-100 | Avg Prediction Set Size47.2 | 32 | |
| Classification | ImageNet | WUC0.019 | 24 | |
| Classification | ImageNet V2 | WUC0.042 | 24 | |
| Classification | CIFAR-100 | WUC0.024 | 24 | |
| Image Classification | ImageNet | Coverage65.8 | 24 | |
| Image Classification | ImageNet V2 | Coverage (Cov)70.8 | 24 | |
| Image Classification | CIFAR-100 | Coverage58.2 | 24 | |
| Conformal Prediction | CIFAR-100 (five repeated splits) | Class Coverage58.2 | 24 | |
| Text Classification | WOS-46985 | Coverage62.1 | 24 |
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