There is a simple rationalization of the difference between the DFT/MRCI(2) and MR-CIS c z 0 (R) surfaces, which differ only in the noise term. Namely, that the MR-CIS potential surfaces do not suffer from the presence of deterministic noise that is inherent to the DFT/MRCI(2) method. However, the same conclusion cannot be drawn for the differences in ω(R). Since we cannot provide physically motivated reasons for the difference in the log-likelihood landscape that arise due to different electronic structure methods, the MLE optimized noise term does not offer insight into the physics of the DFT/MRCI(2) local energetic variation. When the set of optimized hyperparameters {σ 2 , l, σ 2 n } differ significantly, they may correspond to extrema of drastically different log-likelihood landscapes, thus frustrating a simple interpretation. Furthermore, it is also important to remember that the whitenoise kernel hyperparameter σ 2 n is not an absolute measure of noise. Depending on the kernel composition, the log-likelihood surface can differ, even employing the same training data, thus using different kernels changes the learned σ The first and second row corresponds to the learned hyperparameters for the c z 0 (R) and ω(R) surface, respectively. The values presented are the arithmetic mean ± standard deviation over 50 surrogates.