## 3 Results
### CatBoost Combination Feature Construction Results  
The RMSE value is used as an evaluation indicator to optimize tree depth and iteration. The result of the operation is shown in Figure 7. Figure 7 shows that the importance of cement and fly ash is significantly higher than that of other assemblage features. The median and mean values of cement and age were taken as the new characteristics, respectively. The CatBoost algorithm was used to calculate the importance of each feature again, as shown in Figure 8. The importance of the median value of the combined feature was higher than the mean value. The weight of all the features further checked by ELI5 is shown in Table 7. For quantitative comparison of feature importance, the median weight of water and fly ash combination features is 0.0217 \(\pm\) 0.0093, while the mean weight of water and fly ash combination features is 0.0153 \(\pm\) 0.0109. It is observed that the median has a bigger impact on the forecast. ELI5 is a Python package for examining machine learning machines and interpreting their predictions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **R\(-\)Square** & **AIC** & **BIC** \\ \hline Primary Data & 0.860 & 498.1 & 519.2 \\ Revised data & 0.900 & 490.3 & 511.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The changes of R\(-\)Square, AIC and BIC before and after the removal of outliers were compared.  
Figure 6: OLS was used for regression analysis: (**a**) OLS was used for regression analysis of 103 original data. (**b**) After the 82 nd abnormal sample was removed, 102 data were regression-analyzed by OLS.  
Figure 8: Feature importance of new feature set: The median of the combined characteristics of cement and fly ash was more important than its mean.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weight** & **Feature** \\ \hline
0.5153 \(\pm\) 0.4424 & cement \\ \hline
0.4739 \(\pm\) 0.1984 & ash \\ \hline
0.1865 \(\pm\) 0.0590 & water \\ \hline
0.0495 \(\pm\) 0.0405 & slag \\ \hline
0.0421 \(\pm\) 0.0184 & coarseagg \\ \hline
0.0228 \(\pm\) 0.0180 & fineagg \\ \hline
0.0217 \(\pm\) 0.0093 & cement\_ash\_median \\ \hline
0.0153 \(\pm\) 0.0109 & cement\_ash\_mean \\ \hline
0.0069 \(\pm\) 0.0095 & superplastic \\ \hline \hline \end{tabular}
\end{table}
Table 7: EL15 feature weight. The median importance of cement and fly ash combination characteristics was 0.0217 \(\pm\) 0.0093, higher than the mean of 0.0153 \(\pm\) 0.0109. Cement\_ash\_median indicates the median value of combination features, and cement\_ash\_mean indicates the mean value of combination features.  
Figure 7: Importance of full combination characteristics: The importance of cement and fly ash combination is much higher than that of other characteristics, and is about twice of that of water and coarse aggregate.  
Because of the high correlation between the median and mean of water and fly ash combination features, repeated feature learning is selected. We chose to delete the mean value of combination features and retain the median value of combination features. In this paper, by assessing strong combination features, we can effectively use the connection between features, which greatly enriches the feature dimension.  
Finally, the CatBoost machine learning regression prediction model is selected to verify the improvement effect of the new combined feature set on the prediction accuracy, and the mean value of the verification set, and the test set is used as the evaluation criterion. By dividing the dataset into three parts, 71 samples (60%) were used for the training set, 10 samples (10%) were used for the validation set, 21 samples (20%) were used for the test set, and 8-fold cross-validation was adopted. Test results shows that the prediction effect of the model is better improved. The average \(R^{2}\) for validation sets and test sets improved by about 3.33%, which indicates that the new strong features are effective in improving the accuracy of quality prediction.