The fundamental concept behind GPR involves modeling the relationship between input features (predictors) and output variables (predictions) using Gaussian processes (GPs). GPs are defined by a mean function and a covariance function (also known as kernel function). The mean function represents the average trend of the data, while the covariance function captures the similarity between pairs of data points x and x'. The GPR is mathematically represented by 𝑓~𝐺𝑃(𝑚(𝑥), 𝐾(𝑥, 𝑥 ′ )), where the function 𝑓 has a GP distribution with mean function (𝑚) and covariance function (𝐾). Here, as K we use the rational quadratic (RQ) kernel, which takes the mathematical form: