We propose a two-stage machine learning model to predict the HER by combining a first regression model that predicts the HER quantitatively with a second classification model that determines the reliability of the values predicted by the first regression model. Approaches combining models have been reported in the field of MI. Examples of these investigations are as follows. Sakaushi and coauthors have dealt with the loop between material synthesis and machine learning . In this study, a seamless loop between prediction by machine learning and material development was achieved using Bayesian optimization to determine the subsequent experimental conditions for 11 composition-related variables. This study assumes that the properties are uniquely determined by chemical composition. Talapatra and coauthors investigated bandgaps using density functional theory calculations . Parts of the data with a certain threshold value were removed to obtain an accurate prediction model. Balachandran and coauthors also investigated perovskite compounds . They classified perovskites or non-perovskites in the first step and cubic or non-cubic in the second step. Here, we discuss our attempts to deal with biased datasets without data thinning.