Here, ḡ(x) is the predicted mean of g(x). c(x) denotes the vector of covariances between the test point x and the other points in the data set, I is the identity matrix, C the covariance matrix of the data vector, and σ 2 the noise variance. Both C and σ 2 are calculated and optimized in the GPR algorithm implementation provided by the python package sklearn. The algorithm was applied to all possible subsets of adjacent data points in a moving frequency range of two decades inside one EIS measurement. Each frequency data point ω was subsequently assigned a noise level that was calculated as the average of the noise level estimation results σ GPR (S) of all subsets S that this data point was part of,