GPs are defined as a finite or infinite set of random variables such that the joint distribution of any finite subset of these variables follows a multivariate Gaussian distribution . Given an unknown function being observed, GPs can then regress and predict it using a prior distribution and a set of assumptions on the properties of the function . Through GPs, the uncertainty of regression and prediction can be quantified. So far, GPs have been applied to filter data , predict parameters in various contexts , and optimize experiments in the field of active learning . As for EIS, Liu and Ciucci leveraged GPs to deconvolve the distribution of relaxation times, an emerging EIS analysis method . Then, this framework to constrain the DRT to be non-negative using a finite GP approximation . Moreover, Ciucci used GPs complying with the Hilbert transform to evaluate EIS spectra quality . This work develops three new ways in which GPs augment experiments. First, we show that GPs can filter and smooth noisy EIS data. Next, we detail how the polarization resistance, 𝑅 p , can be understood as a stochastic Gaussian variable, and we use this insight to predict this critical parameter. Lastly, we develop a new GP-based, active-learning acquisition method to select frequencies optimally. Our approach enables data quality improvement while bounding the experimental time. Figure shows how GPs can filter EIS data and how frequencies can be selected sequentially for optimal acquisition. In short, this work lays the foundations for GP-based probabilistic methods for the analysis and prediction of EIS spectra. Figure . GPs can be used to filter an EIS spectrum and select frequencies optimally through active learning.