In the present study, a two-stage machine learning model was constructed for the skewed HER data of metal-sulfide photocatalysts, which are considered difficult to predict. The results showed that this machine learning model improved the prediction accuracy of the HER compared with that of the one-stage regression model. The proposed two-stage machine learning model is available with various regression models and can be applied to a wide range of tasks.