## 4 Results
### Comparison among Different Classifiers
#### 4.2.4 Ensemble Method  
EM stands out as a different approach, since it handles different algorithms to attain the optimal prediction [45]. It can be thus conceived as a family of algorithms, rather than a unique approach. The bootstrap aggregation (bagged tree) is the most famous ensemble classifier. In this work, RUSBoost, bagged tree (Bag), and AdaBoost algorithms are adopted, with: maximum number of split varied between 1 and 107; number of learners ranging from 10 to 500; learning rate varied between 0.001 and 1; predictor samples in the range from 1 to 8. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Model** & **Kernel Function** & **Kernel Scale** & **Multi-Class Method** & **Optimizer** & **Iterations** & **Excl. Feat.** & **PCA** & **Training Accuracy (\%)** & **Testing Acc. (\%)** & **Training Time (s)** \\ \hline S1 & Gaussian & 15.96 & one-vs-one & Bayesian & 40 & - & no & 95.4 & 94.4 & 134.6 \\ S2 & linear & 10 & one-vs-one & grid search & grid div = 10 & - & no & 95.4 & 94.4 & 2030 \\ S3 & linear & 2.14 & one-vs-one & random search & 40 & - & no & 95.4 & 94.4 & 223.5 \\ S4 & quadratic & 133.56 & one-vs-one & Bayesian & 40 & \(\star\) & no & 95.4 & 91.7 & 139.8 \\ S5 & Gaussian & 0.12 & one-vs-all & Bayesian & 40 & - & yes & 86.1 & 91.7 & 137.8 \\ S6 & linear & 99.79 & one-vs-one & Bayesian & 40 & \(\star\) & yes & 91.7 & 91.7 & 152.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Features of the SVM models, and relevant accuracies achieved. Excl. feat. = excluded features (\(\star\) = top/bottom geometry, diagrid total length, maximum normal force). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Model** & **k** & **Distance Metric** & **Distance Weight** & **Optimizer** & **Iterations** & **Excl. Feat.** & **PCA** & **Training Accuracy (\%)** & **Testing Acc. (\%)** & **Training Time (s)** \\ \hline K1 & 1 & city block & inverse & grid search & grid div = 10 & - & no & 91.7 & 94.5 & 121.1 \\ K2 & 2 & city block & squared inverse & Bayesian & 30 & - & no & 91.7 & 94.4 & 38.1 \\ K3 & 2 & city block & inverse & random search & 30 & - & no & 91.7 & 94.4 & 15.9 \\ K4 & 3 & city block & squared inverse & Bayesian & 50 & \(\star\) & no & 96.3 & 97.2 & 46.3 \\ K5 & 9 & city block & squared inverse & Bayesian & 50 & - & no & 91.7 & 97.2 & 64.5 \\ K6 & 15 & Euclidean & squared inverse & Bayesian & 50 & \(\star\) & yes & 88.0 & 97.2 & 54.3 \\ K7 & 54 & Mahalanobis & squared inverse & Bayesian & 50 & - & yes & 88.0 & 94.4 & 55.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features of the kNN models, and relevant accuracies achieved. Excl. feat. = excluded features (\(\star\) = top/bottom geometry, diagrid total length, maximum normal force). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Model** & **Max Split** & **Split Criterion** & **Surrogate Decision Split** & **Optimizer** & **Iterations** & **Excl. Feat.** & **PCA** & **Training Accuracy (\%)** & **Testing Acc. (\%)** & **Training Time (s)** \\ \hline T1 & 5 & Twoing & - & Bayesian & 40 & - & no & 93.5 & 100.0 & 29.7 \\ T2 & 4 & Gini & max 10 sur. & random search & 40 & - & no & 93.5 & 100.0 & 17.4 \\ T3 & 7 & Gini & all & random search & 40 & - & no & 93.5 & 100.0 & 18.8 \\ T4 & 42 & Gini & all & random search & 40 & - & yes & 86.1 & 77.8 & 30.8 \\ T5 & 5 & Gini & off & random search & 40 & top/bottom geom. & no & 93.5 & 100.0 & 45.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Features of the DT models, and relevant accuracies achieved. Excl. feat. = excluded features. Results are characterized by a training accuracy of 85.2-98.1%, a testing accuracy of 94.4-100%, and a computing time ranging from 72 s to 129.4 s. The performances have a relevant scattering depending on the choice of the algorithm in the family, see Table 6. For models E1, E2, and E3, the Bayesian, grid search, and random search algorithms are adopted, and only with the grid search one the accuracy slightly decreases, even if the computing time is the largest.