Nuclear separation energies play pivotal roles in determining nuclear reaction rates and thus significantly impact astrophysical nucleosynthesis processes. The separation energies of many neutron-rich nuclei are still beyond the capacity of experimental measurements even in the foreseeable future.

This study aims to employ two machine learning approaches to improve nuclear separation energy predictions, including double neutron (S_2n), double proton (S_2p), single neutron (S_n), and single proton (S_p) separation energies.

The Kernel Ridge Regression (KRR) and Kernel Ridge Regression with odd-even effects (KRRoe) approaches were applied to predict nuclear masses. Nuclear separation energies were calculated with the KRR and KRRoe mass models. The accuracies of these two approaches in describing experimentally known separation energies were compared. In addition, the extrapolation performances of KRR and KRRoe approaches for single nucleon separation energy and double nucleon separation energy were also compared.

Both KRR and KRRoe methods improve descriptions of double nucleon separation energies S_2n and S_2p. However, only the KRRoe method achieves enhanced improvement for single nucleon separation energies S_n and S_p, owing to its kernel function that incorporates odd-even effects, effectively capturing the staggering behavior in these energies, unlike the KRR's flat Gaussian kernel.

The study demonstrates the importance of incorporating odd-even effects to accurately describe single nucleon separation energies, highlighting the superiority of the KRRoe method over the standard KRR method in the predictions of single nucleon separation energies.