Figure shows the effects of the combination of regression using the first model and classification using the second model on the prediction accuracy. The color of the plot in Figure (a) indicates the probability value of positive decisions in the second model. Figure (a) shows a distorted shape with a shoulder near zero owing to the biased HER distribution. Figure (b) and (c) depict the impact of the classification of Figure (a) based on the score of the probability of positive (the threshold was 0.5). In Figure (b), the distortion is improved, and the plot points are closer to the ideal line indicated by the dotted line. However, Figure (c) shows that the plot points were generally far from the ideal line. The coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) indicated better accuracy for the data with positive judgments and worse accuracy for the data with negative judgments, compared to the case of all data. In conclusion, the proposed two-stage machine learning model was effective in improving the prediction accuracy of the HER.