With the theoretical and experimental parameters in hand, Gaussian process regression (GPR), which is a radial basis function kernel-based statistical-learning algorithm, using GPy (a programming library for GPR), was applied to construct a model to predict the TOF values for the production of 3aa (it should be noted here that unless stated otherwise, mean values are presented for the theoretically predicted TOF values). GPR using GPy constructs a regression model using a limited number of observed data through ML and searches for a subsequent adequate parameter value of B xy using the surrogate model. We evaluated the accuracy of each GPR model based on the coefficient of determination (Q 2 ) of the leave-one-out (LOO) cross-validation using either training or validation data.