## 3 Results
### Proactive System Based on Predictions
#### 3.2.1 Prediction Models Applying Classical Machine Learning Methods
Primary and Secondary Coating Area Predictions  
The following experiments, illustrated in Table 3, focused on predicting elongation using data from both primary and secondary coatings. The best classifier in this case was the Bayesian network trained with the K2 algorithm. However, when comparing these results with those obtained using only primary coating information, it appears that incorporating secondary coating data introduced noise, leading to a decrease in classifier performance. Again, another classifier that closely approaches the best performance is the MLP artificial neural network with backpropagation. Additionally, the SVM trained with the Pearson VII kernel achieved accuracy values nearly identical to the Bayesian network, differing by only a few decimals. All random forests performed similarly and were very close to the best SVM. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Bayes network (K2) & 62.75 & 0.39 & 0.47 & 0.65 \\ Bayes network (hill climber) & 63.62 & 0.43 & 0.49 & 0.47 \\ Bayes network (TAN) & 67.07 & 0.37 & 0.46 & 0.68 \\ \hline Naive Bayes & 64.05 & 0.39 & 0.46 & 0.68 \\ \hline Artificial neural network MLP & 74.98 & 0.27 & 0.41 & 0.77 \\ \hline Support vector machines (polynomial kernel) & 58.46 & 0.42 & 0.64 & 0.44 \\ Support vector machines (normalized polynomial kernel) & 65.87 & 0.34 & 0.58 & 0.48 \\ Support vector machines (Pearson VII) & 75.43 & 0.25 & 0.47 & 0.64 \\ Support vector machines (radial basis function) & 68.75 & 0.31 & 0.56 & 0.50 \\ \hline Voted perceptron & 64.04 & 0.36 & 0.58 & 0.61 \\ \hline \hline K-nearest neighbors (K = 1) & 75.73 & 0.32 & 0.44 & 0.71 \\ \hline K-nearest neighbors (K = 2) & 74.52 & 0.35 & 0.45 & 0.66 \\ K-nearest neighbors (K = 3) & 66.09 & 0.40 & 0.47 & 0.56 \\ K-nearest neighbors (K = 4) & 67.04 & 0.40 & 0.47 & 0.57 \\ K-nearest neighbors (K = 5) & 65.86 & 0.40 & 0.46 & 0.61 \\ K-nearest neighbors (K = 6) & 65.62 & 0.40 & 0.46 & 0.64 \\ \hline C4.5 & 67.79 & 0.43 & 0.47 & 0.49 \\ \hline Random Forest (Num. Iterations = 50) & 68.20 & 0.38 & 0.47 & 0.64 \\ Random forest (num. iterations = 100) & 69.05 & 0.38 & 0.47 & 0.64 \\ Random forest (num. iterations = 150) & 69.59 & 0.38 & 0.47 & 0.64 \\ Random forest (num. iterations = 200) & 68.79 & 0.38 & 0.47 & 0.64 \\ Random forest (num. iterations = 250) & 69.16 & 0.38 & 0.47 & 0.64 \\ Random forest (num. iterations = 300) & 68.62 & 0.38 & 0.47 & 0.65 \\ Random forest (num. iterations = 350) & 68.21 & 0.38 & 0.47 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results predicting porosity apparition when we applied classical machine learning methods using data extracted only from the primary coating area. The best classifier, marked in gray, was the kNN with \(k=1\) with an accuracy level (percentage of correctly classified instances) close to 76%. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline
**Bayes network (K2)** & 76.95 & 0.26 & 0.40 & 0.84 \\ \hline Bayes network (hill climber) & 75.64 & 0.28 & 0.42 & 0.82 \\ Bayes network (TAN) & 72.24 & 0.31 & 0.43 & 0.80 \\ \hline Naive Bayes & 74.76 & 0.27 & 0.41 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results predicting the mechanical property elongation when we applied classical machine learning methods using data extracted from both primary and secondary coating areas. The best classifier, marked in gray, was the Bayesian network learned taught with the K2 algorithm, with an accuracy level (percentage of correctly classified instances) close to 77%. Then, regarding the prediction of porosity formation using primary and secondary coating information, Table 4 summarizes the measured results. In this case, the best-performing classifier was kNN with a value of \(k=6\). Its accuracy was a 71%.