## 4 Results
### Computational Performance  
The models' computational performance results are disclosed in Table 5. The prediction time refers to the interval when the model calculates the value of the target variable based on the manufacturing properties. At the same time, the size represents the amount of space that the model occupies in a storage disk. One can notice that both metrics follow the same pattern independently of the target variable. Despite their poor performance discussed previously, the linear models have shown to be considerably faster and smaller  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Mean** & **Std Dev** & **Min.** & **1 st Quartile** & **Median** & **3 rd Quartile** & **Max** \\ \hline BAY & 159.7859 & 24.6198 & 131.2626 & 143.6800 & 156.5881 & 166.6547 & 258.1069 \\ RDG & 159.4300 & 25.2739 & 128.5327 & 143.7674 & 155.4155 & 167.4714 & 258.7639 \\ LAS & 159.3340 & 25.2431 & 129.0285 & 143.4035 & 155.3209 & 166.2181 & 258.7624 \\ DTR & 152.2683 & 45.3625 & 76.5504 & 115.0226 & 150.2737 & 170.3594 & 289.0174 \\ SVR & 142.2886 & 43.2337 & 71.5865 & 115.0798 & 144.5133 & 164.2971 & 258.5963 \\ KNN & 121.4163 & 42.1382 & 53.4138 & 91.3910 & 111.6907 & 141.3862 & 251.2954 \\ CAT & 114.4038 & 32.6867 & 54.2600 & 91.3665 & 109.3586 & 139.4164 & 197.8857 \\ RFR & 113.1730 & 36.8256 & 52.8864 & 84.8905 & 107.1129 & 136.4452 & 209.2337 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE summary statistics for the one hundred tests predicting \(\sigma\).  
Figure 8: RMSE boxplots for (**a**) \(\sigma\) and (**b**) E.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Mean** & **Std Dev** & **Min.** & **1 st Quartile** & **Median** & **3 rd Quartile** & **Max** \\ \hline BAY & 16.2405 & 3.2572 & 9.4879 & 14.0424 & 15.8779 & 18.3325 & 24.2133 \\ RDG & 16.2256 & 3.4534 & 9.0721 & 13.8824 & 16.0427 & 18.6324 & 25.0582 \\ LAS & 16.1670 & 3.2741 & 8.9366 & 14.1336 & 15.6067 & 18.0086 & 23.7556 \\ DTR & 14.8774 & 4.3100 & 7.6346 & 12.0422 & 14.4884 & 16.7512 & 27.0928 \\ SVR & 11.7524 & 5.0485 & 2.9113 & 7.6782 & 12.4260 & 15.4035 & 21.4587 \\ RFR & 9.8297 & 3.6813 & 3.6341 & 6.8087 & 8.6466 & 12.3373 & 19.6891 \\ KNN & 9.8020 & 4.1159 & 3.7364 & 6.6482 & 9.3383 & 11.9770 & 23.3197 \\ CAT & 9.4446 & 3.3558 & 3.0735 & 7.2028 & 8.8084 & 11.7664 & 18.1977 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE summary statistics for the one hundred tests predicting E.  
than the KNN and CAT. The tree-based model can also be considered fast as it can make a prediction in less than 1 ms, despite being one of the largest models. Moreover, despite being able to make a prediction in more than 10 ms, the k-neighbor model is at least ten-fold smaller than the ensemble. Therefore, there is a clear size versus prediction time tradeoff between the CAT and KNN, the models with the best regression performance.  
In summary, the linear models can serve as a baseline and have proven inadequate for predicting the mechanical properties of AM composite parts despite having the lowest computational complexity. On the other hand, both the KNN and CAT presented satisfactory regression performances, thus being suitable for performing the task at hand. The goal of assessing metrics like size and prediction time is to evaluate the model with the best balance between the regression and computational performance. In this context, the size versus time tradeoff between the tree-based and the instance-based models makes the analysis difficult. However, in assuming that the predictive model will not be embedded in a device with restricted hardware, the prediction time can be used as the first criterion to define the more balanced candidate. In this sense, the CAT can be considered the most suitable solution to use as a predictive model for estimating the mechanical properties of AM composite parts based on their printing variables.