## 3 Results
### Proactive System Based on Predictions
#### 3.2.1 Prediction Models Applying Classical Machine Learning Methods
As with the elongation prediction, we observe that introducing secondary coating data leads to a decline in results.In this case, there were only two classifiers that reached or exceed 70% accuracy, the aforementioned kNN with \(k=6\) and the voted perceptron. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Bayes network (K2) & 66.36 & 0.39 & 0.45 & 0.66 \\ Bayes network (hill climber) & 63.50 & 0.44 & 0.49 & 0.49 \\ Bayes network (TAN) & 68.62 & 0.41 & 0.47 & 0.61 \\ \hline Naive Bayes & 68.80 & 0.39 & 0.45 & 0.68 \\ \hline Artificial neural network MLP & 62.02 & 0.42 & 0.49 & 0.59 \\ \hline Support vector machines (polynomial kernel) & 66.96 & 0.33 & 0.57 & 0.49 \\ Support vector machines (normalized polynomial Kernel) & 66.84 & 0.33 & 0.57 & 0.51 \\ Support vector machines (Pearson VII) & 67.93 & 0.32 & 0.55 & 0.56 \\ Support vector machines (radial basis function) & 68.75 & 0.31 & 0.56 & 0.50 \\ \hline Voted perceptron & 70.09 & 0.30 & 0.53 & 0.66 \\ \hline K-nearest neighbors (K = 1) & 69.55 & 0.39 & 0.48 & 0.63 \\ K-nearest neighbors (K = 2) & 69.84 & 0.39 & 0.48 & 0.63 \\ K-nearest neighbors (K = 3) & 69.84 & 0.40 & 0.48 & 0.62 \\ K-nearest neighbors (K = 4) & 69.84 & 0.41 & 0.47 & 0.62 \\ \hline \end{tabular}
\end{table} 
Table 4: Results predicting porosity apparition 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 kNN with \(k=6\) and an accuracy level (percentage of correctly classified instances) close to 71%. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Artificial neural network MLP & 75.21 & 0.27 & 0.43 & 0.82 \\ \hline Support vector machines (polynomial kernel) & 72.52 & 0.27 & 0.49 & 0.73 \\ Support vector machines (normalized polynomial kernel) & 74.48 & 0.26 & 0.46 & 0.75 \\ Support vector machines (Pearson VII) & 76.79 & 0.23 & 0.44 & 0.77 \\ Support vector machines (radial basis function) & 73.93 & 0.26 & 0.47 & 0.75 \\ \hline Voted perceptron & 72.57 & 0.27 & 0.48 & 0.80 \\ \hline K-nearest neighbors (K = 1) & 65.79 & 0.32 & 0.50 & 0.74 \\ K-nearest neighbors (K = 2) & 66.05 & 0.33 & 0.44 & 0.78 \\ K-nearest neighbors (K = 3) & 68.60 & 0.36 & 0.43 & 0.78 \\ K-nearest neighbors (K = 4) & 69.50 & 0.36 & 0.44 & 0.78 \\ K-nearest neighbors (K = 5) & 69.50 & 0.36 & 0.44 & 0.78 \\ K-nearest neighbors (K = 5) & 69.50 & 0.36 & 0.44 & 0.78 \\ K-nearest neighbors (K = 6) & 72.55 & 0.36 & 0.43 & 0.78 \\ \hline C4.5 & 66.24 & 0.38 & 0.48 & 0.80 \\ \hline Random forest (num. iterations = 50) & 74.10 & 0.34 & 0.42 & 0.80 \\ Random forest (num. iterations = 100) & 74.24 & 0.34 & 0.42 & 0.80 \\ Random forest (num. iterations = 150) & 74.38 & 0.34 & 0.42 & 0.79 \\ Random forest (num. iterations = 200) & 74.21 & 0.34 & 0.42 & 0.79 \\ Random forest (num. iterations = 250) & 74.36 & 0.34 & 0.42 & 0.79 \\ Random forest (num. iterations = 300) & 74.07 & 0.34 & 0.42 & 0.79 \\ Random forest (num. iterations = 350) & 74.05 & 0.34 & 0.42 & 0.79 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Primary, Secondary Coatings and Melting Area Predictions  
Finally, using the complete dataset (i.e., data from the three selected areas), the accuracy of the classical machine learning models was tested. First, we extracted the results for elongation prediction, which are shown in Table 5. With the full dataset, the classifiers once again approximated their results to those obtained when only primary coating data were included in the predictive models. In this experiment, we observe that the best classifier, kNN with \(k=6\), is able to match or even outperform the best classifier from the first analysis (another kNN, but in that case with \(k=2\)). Comparing the other classifiers, they achieved similar accuracy levels and, in fact, were close to the results obtained when using other subsets of information for their learning phase.