## 3 Results
### Proactive System Based on Predictions
#### 3.2.1 Prediction Models Applying Classical Machine Learning Methods
Although the best result was obtained with the full dataset (including data from all three areas), it is not certain that the performance of the classifiers improves when using the complete dataset. This leads us to conclude that, in predicting the elongation of a part at a future state \(t+1\), the primary coating stage is the most influential factor affecting the final behavior of this mechanical property. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline K-nearest neighbors (K = 5) & 69.84 & 0.41 & 0.47 & 0.64 \\ K-nearest neighbors (K = 6) & **71.48** & **0.41** & **0.47** & **0.65** \\ \hline C4.5 & 63.91 & 0.45 & 0.48 & 0.46 \\ \hline Random forest (num. iterations = 50) & 65.66 & 0.41 & 0.47 & 0.62 \\ Random forest (num. iterations = 100) & 66.20 & 0.41 & 0.47 & 0.62 \\ Random forest (num. iterations = 150) & 66.48 & 0.41 & 0.47 & 0.62 \\ Random forest (num. iterations = 200) & 67.04 & 0.41 & 0.47 & 0.62 \\ Random forest (num. iterations = 250) & 67.45 & 0.41 & 0.47 & 0.61 \\ Random forest (num. iterations = 300) & 66.91 & 0.41 & 0.47 & 0.61 \\ Random forest (num. iterations = 350) & 67.34 & 0.41 & 0.47 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Bayes network (K2) & 73.83 & 0.29 & 0.40 & 0.83 \\ Bayes network (hill climber) & 73.45 & 0.33 & 0.43 & 0.79 \\ Bayes network (TAN) & 76.52 & 0.30 & 0.40 & 0.81 \\ \hline Naive Bayes & 75.93 & 0.29 & 0.39 & 0.83 \\ \hline Artificial neural network MLP & 73.98 & 0.28 & 0.44 & 0.79 \\ \hline Support vector machines (polynomial kernel) & 71.48 & 0.29 & 0.50 & 0.72 \\ Support vector machines (normalized polynomial kernel) & 75.62 & 0.24 & 0.44 & 0.76 \\ Support vector machines (Pearson VII) & 72.93 & 0.27 & 0.48 & 0.73 \\ Support vector machines (radial basis function) & 60.38 & 0.40 & 0.61 & 0.64 \\ \hline Voted perceptron & 72.69 & 0.27 & 0.47 & 0.80 \\ \hline K-nearest neighbors (K = 1) & 72.07 & 0.36 & 0.50 & 0.68 \\ K-nearest neighbors (K = 2) & 73.93 & 0.37 & 0.46 & 0.75 \\ K-nearest neighbors (K = 3) & 78.83 & 0.36 & 0.43 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results predicting the mechanical property elongation when we applied classical machine learning methods using data extracted from both primary and secondary coating areas in addition to melting data. The best classifier, marked in gray, was the kNN with \(k=6\), achieving an accuracy level (percentage of correctly classified instances) close to 80%. Second, we tested how well the models performed in predicting the appearance of porosities. Considering all the results achieved using the three different datasets, we can confirm that ex ante detection of this problem is more complex than predicting elongation. Additionally, when analyzing the results in Table 6, we observed once again that data from the secondary coating and melting stages introduced noise and negatively affected classifier performance. In this case, the naive Bayes classifier achieved the best results, surpassing the kNN with \(k=1\) from the first analysis, which used only primary coating data.