As shown in Fig. , a bias remains in the predictions considering all materials and processes, and in this section, we analyze the reason in detail. First, the data vectorized by the best representation (1st split and zero complements) were embedded in two dimensions by t-distributed stochastic neighbor embedding, t-SNE (Fig. ). Each plot was colored depending on experimental PCE. Ideally, if close vectors have similar PCE, the color gradation would be observed. Plots with higher PCE (shown in red) were relatively assembled in the lower right area, and plots with smaller PCE (shown in blue) were in the left area. However, color gradation was unclear because many areas have a mixed distribution of red and blue plots. It means that close vectors have largely different PCE, leading to reduce the accuracy of prediction.