  General predictive models do not provide a measure of confidence in
predictions without Bayesian assumptions. A way to circumvent potential
restrictions is to use conformal methods for constructing non-parametric
confidence regions, that offer guarantees regarding validity. In this paper we
provide a detailed description of a computationally efficient conformal
procedure for Kernel Ridge Regression (KRR), and conduct a comparative
numerical study to see how well conformal regions perform against the Bayesian
confidence sets. The results suggest that conformalized KRR can yield
predictive confidence regions with specified coverage rate, which is essential
in constructing anomaly detection systems based on predictive models.
