Developing machine learning frameworks with predictive power, interpretability, and transferability is crucial, yet faces challenges in the field of electrocatalysis. To achieve this, we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor (GBR) model, which adeptly captures the physical complexity from feature space to target variables. We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations. The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies ($R_{\text{ave}}^2$$R_{\text{ave}}^2$ = 0.937, RMSE = 0.153 eV). Moreover, the model demonstrated remarkable transfer learning ability, showing excellent predictive power for OH, NO, and N₂ adsorption. Importantly, the GBR model exhibits exceptional predictive capability across an extensive search space, thereby demonstrating profound adaptability and versatility. Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis, offering vital insights for further advancements.

开发具有预测能力、可解释性和可转移性的机器学习框架至关重要，但在电催化领域面临着挑战。为了实现这一目标，我们采用严格的特征工程来建立一个微调的梯度增强回归器（GBR）模型，该模型能够熟练地捕获从特征空间到目标变量的物理复杂性。我们通过全局和局部解释证明，环境电子效应和原子序数在很大程度上决定了绘图过程的成功。经过微调的 GBR 模型在预测 CO 吸附能方面表现出卓越的鲁棒性（ $R_{\text{ave}}^2$ = 0.937，RMSE = 0.153 eV）。此外，该模型表现出卓越的迁移学习能力，对 OH、NO 和 N ₂ 吸附表现出出色的预测能力。重要的是，GBR 模型在广泛的搜索空间中表现出卓越的预测能力，从而展示了深刻的适应性和多功能性。我们的研究框架显着增强了电催化中机器学习的可解释性和可转移性，为进一步发展提供了重要的见解。