## 3 Results
### Primary and Secondary Coating Area Predictions
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline By vote (minimum probability) & 59.91 & 0.28 & 0.52 & 0.56 \\ By vote (maximum probability) & 68.75 & 0.42 & 0.46 & 0.60 \\ \hline By vote (average of probabilities) & 71.27 & 0.39 & 0.47 & 0.63 \\ \hline By vote (product of probabilities) & 59.91 & 0.28 & 0.52 & 0.56 \\ By vote (majority voting) & 69.80 & 0.30 & 0.54 & 0.58 \\ \hline Grading (meta: naïve Bayes) & 70.12 & 0.30 & 0.53 & 0.58 \\ Grading (Bayes network–TAN) & 70.84 & 0.29 & 0.52 & 0.59 \\ Grading (meta: K-nearest neighbors with K = 1) & 70.27 & 0.30 & 0.53 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results predicting the porosity apparition when we applied a combination of classical machine learning methods through a meta-classifier using data extracted from both primary and secondary coating areas. The best meta-classifier, marked in gray, was by vote through the average of probabilities, with an accuracy level (percentage of correctly classified instances) of close to 71%. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline By vote (minimum probability) & 63.00 & 0.19 & 0.35 & 0.75 \\ By vote (maximum probability) & 73.83 & 0.40 & 0.43 & 0.81 \\ \hline By vote (average of probabilities) & 76.50 & 0.31 & 0.40 & 0.83 \\ By vote (product of probabilities) & 63.00 & 0.19 & 0.35 & 0.75 \\ By vote (majority voting) & 74.95 & 0.25 & 0.46 & 0.75 \\ \hline Grading (meta: naïve Bayes) & 73.86 & 0.26 & 0.47 & 0.74 \\ Grading (Bayes network–TAN) & 74.05 & 0.26 & 0.47 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 1) & 74.12 & 0.26 & 0.47 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 2) & 72.60 & 0.27 & 0.49 & 0.73 \\ Grading (meta: K-nearest neighbors with K = 3) & 73.14 & 0.27 & 0.48 & 0.73 \\ Grading (meta: K-nearest neighbors with K = 4) & 73.00 & 0.27 & 0.48 & 0.73 \\ Grading (meta: K-nearest neighbors with K = 5) & 73.29 & 0.27 & 0.48 & 0.73 \\ Grading (C4.5) & 74.17 & 0.26 & 0.47 & 0.74 \\ \hline Stacking (meta: naïve Bayes) & 75.12 & 0.25 & 0.46 & 0.80 \\ Stacking (Bayes network–TAN) & 70.05 & 0.30 & 0.48 & 0.77 \\ Stacking (meta: K-nearest neighbors with K = 1) & 67.29 & 0.33 & 0.53 & 0.68 \\ Stacking (meta: K-nearest neighbors with K = 2) & 66.86 & 0.33 & 0.48 & 0.72 \\ Stacking (meta: K-nearest neighbors with K = 3) & 68.90 & 0.33 & 0.46 & 0.74 \\ Stacking (meta: K-nearest neighbors with K = 4) & 70.05 & 0.33 & 0.45 & 0.76 \\ Stacking (C4.5) & 72.00 & 0.33 & 0.44 & 0.77 \\ \hline Multi-scheme (Bayes network–TAN) & 74.67 & 0.25 & 0.44 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results predicting the mechanical property elongation when we applied a combination of classical machine learning methods through a meta-classifier using data extracted from both primary and secondary coating areas. The best meta-classifier, marked in gray, was by vote through the average of probabilities, with an accuracy level (percentage of correctly classified instances) of close to 77%. Primary, Secondary Coatings and Melting Area Predictions  
Continuing, when we applied data from the three identified stages, we analyzed the prediction of elongation. The obtained results are shown in Table 11. In this experiment, the meta-classification methods were unable to achieve the same performance ratios as the classical ones. In this evaluation, once again, grading proved to be the most stable algorithm, obtaining very similar accuracy values across all tested methods, as well as consistent error-handling measurements. Nevertheless, it is surprising that for this prediction task and dataset, the highest-rated algorithm was one of those created using stacking. It appears to have been better at managing the noise introduced by the addition of new information. Despite this, grading remained close behind. Later, in search of a simpler approach, we re-evaluated the simplest algorithms, such as majority voting.