When searching for a photocatalyst with high HER activity using the constructed two-stage machine learning model, it is necessary to define variables related to the experimental conditions in addition to the chemical composition. Because the optimal set of variables for the experimental conditions for the chemical compositions to be explored is unknown, it is desirable to consider as many diverse sets of variables related to experimental conditions as possible. Furthermore, the considered sets should be in a range in which the two-stage machine learning model can be driven well. We also propose a search scheme for variables related to the experimental conditions in anticipation of a photocatalyst search utilizing this two-stage machine-learning model. Kudo et al. has developed a number of metal-sulfide photocatalysts capable of producing hydrogen from an aqueous solution containing sacrificial reagents under visible light irradiation. For example, these data were used in this study . 577 experimental data for these photocatalysts were used in this study. This dataset consisted of material variable (chemical composition), variables related to the experimental conditions (90 variables related to synthesis conditions and reaction conditions), and objective variable (HER). In this dataset, sacrificial hydrogen evolution was conducted under visible light irradiation (λ > 420 nm) using 300 W Xe lamp attached with a cut-off filter (L42, HOYA). Figure (a) presents an overview of the dataset. As shown in Figure , the distribution of HER was skewed toward smaller values.