The significance values 𝜆𝜆 after applying PCA on chemical composition data are listed in Table . By summing up the significance values of the principle components PC1-PC3, we find that three PCs are sufficient to explain 99.80% of the variations within the dataset. When only PC1 and PC2 are used, this value reduces to 90.62 %. Thus, using more than three variables (genes) will probably overfit the current dataset, while using only two variables leads to underfitting. This reduction in dataset dimensionality (from 6 to 3) can be understood intuitively from the correlation between anion and cation fractions (charge compensation), mass conservation, and possible systematic correlations produced by interrelated batching (e.g., when using Na2SO4 or AlF3 for batching S or F). This reflects the PCA, where an increase in confidence from ~ 70 % to ~ 90 % was seen when moving from one single to two PCs. In Group III, the linearity further improves slightly, with most models achieving R² above 99.2%. Groups IV, V, and VI show no more significant improvement in the relative R 2 value, with almost all models hitting 100%. Although we are not optimizing for the maximum variance such as in the PCA, group-to-group comparison strongly resembles the results of PCA. This is because the total sum of squares in the calculation of the R 2 value is proportional to the variance of data. Finally, the decreasing R 2 with increasing temperature indicates that the conductivity depends less on composition for higher temperatures.