[Background]　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.　[Purpose]　This　study　aims　to　employ　two　machine　learning　approaches　to　improve　nuclear　separation　energy　predictions,　including　double　neutron　(S2n),　double　proton　(S2p),　single　neutron　(Sn),　and　single　proton　(Sp)　separation　energies.　[Methods]　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.　[Results]　Both　KRR　and　KRRoe　methods　improve　descriptions　of　double　nucleon　separation　energies　S2n　and　S2p.　However,　only　the　KRRoe　method　achieves　enhanced　improvement　for　single　nucleon　separation　energies　Sn　and　Sp,　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.　[Conclusions]　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.　©　2025　Science　Press.　All　rights　reserved.