Shapley-value based inductive conformal prediction
Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 152:52-71, 2021.
Shapley values of individual instances were recently proposed for the problem of data valuation. They were defined as the average marginal instance contributions to the performance of a given predictor. In this paper we propose to use Shapley values of individual instances as conformity scores. To compute these values efficiently and exactly we employ a standard algorithm based on nearest neighbor classification and propose a variant of this algorithm for clustered data. Both variants are used for computing Shapley conformity scores for inductive conformal predictors. The experiments show that the Shapley-value conformity scores result in smaller prediction sets for significance level $\epsilon \leq 0.1$ compared with those produced by standard conformity scores (i.e. similarity between true and predicted output values).