## 3 Results
### Primary Coating Area Predictions  
In the first experiment, focused on predicting elongation, we reproduced the previous experiments using the same dataset from the primary coating area to perform meta-classification-based predictions (see results in Table 7). In this case, we observe that, in general, the process remains fairly stable, with most algorithms achieving an accuracy of around 75%. This corroborates the expected performance of meta-classification algorithms. Specifically, the method that obtained the best results was grading using a C4.5 decision tree, achieving an accuracy of 76%, very close to the best classical classifier, which reached 79%. On the other hand, while grading is a more complex method compared to others, the simpler majority voting approach still achieved a notable accuracy of 75%. In fact, it performed almost as well as the best meta-classification method while offering the advantage of a simpler model combination approach.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline By vote (minimum probability) & 50.02 & 0.20 & 0.36 & 0.68 \\ By vote (maximum probability) & 68.40 & 0.41 & 0.45 & 0.77 \\ By vote (average of probabilities) & 74.74 & 0.33 & 0.42 & 0.78 \\ By vote (product of probabilities) & 50.02 & 0.20 & 0.36 & 0.68 \\ By vote (majority voting) & 75.48 & 0.25 & 0.46 & 0.76 \\ \hline Grading (meta: naïve Bayes) & 75.74 & 0.24 & 0.46 & 0.76 \\ Grading (Bayes network–TAN) & 74.62 & 0.25 & 0.47 & 0.75 \\ Grading (meta: K-nearest neighbors with K = 1) & 75.02 & 0.25 & 0.47 & 0.75 \\ Grading (Meta: K-nearest neighbors with K = 2) & 75.17 & 0.25 & 0.46 & 0.75 \\ Grading (Meta: K-nearest neighbors with K = 3) & 75.02 & 0.25 & 0.46 & 0.75 \\ Grading (Meta: K-nearest neighbors with K = 4) & 75.02 & 0.25 & 0.46 & 0.75 \\ Grading (Meta: K-nearest neighbors with K = 5) & 75.02 & 0.25 & 0.46 & 0.75 \\ \hline
**Grading (C4.5)** & 76.07 & 0.24 & 0.45 & 0.76 \\ \hline Stacking (meta: naïve Bayes) & 75.33 & 0.25 & 0.46 & 0.80 \\ Stacking (Bayes network–TAN) & 74.67 & 0.27 & 0.44 & 0.77 \\ Stacking (meta: K-nearest neighbors with K = 1) & 73.40 & 0.28 & 0.48 & 0.73 \\ Stacking (meta: K-nearest neighbors with K = 2) & 70.19 & 0.31 & 0.47 & 0.73 \\ Stacking (meta: K-nearest neighbors with K = 3) & 71.17 & 0.32 & 0.46 & 0.75 \\ Stacking (meta: K-nearest neighbors with K = 4) & 73.52 & 0.32 & 0.44 & 0.76 \\ Stacking (meta: K-nearest neighbors with K = 5) & 73.50 & 0.33 & 0.44 & 0.78 \\ Stacking (C4.5) & 72.90 & 0.31 & 0.46 & 0.74 \\ \hline Multi-scheme (Bayes network–TAN) & 72.07 & 0.33 & 0.48 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results predicting mechanical property elongation when we applied a combination of classical machine learning methods through a meta-classifier using data extracted only from the primary coating area. The best meta-classifier, marked in gray, was the grading method using a C4.5 decision tree for the combination with an accuracy level (percentage of correctly classified instances) of close to 76%.  
Then, using the same dataset, integrating only the primary coating data, we performed the same evaluation to predict the behavior of the manufactured castings in terms of porosity formation. In this experiment (results shown in Table 8), we observed that the best method was multi-scheme, a fairly simple approach that achieved 75% accuracy. This result is comparable to that obtained by the classical unary classifiers analyzed earlier. In any case, when seeking the simplest combination method, classification by majority voting performed very similarly to the best of the combination methods. It should also be noted that the most stable method was grading, as every model achieved close to 75% accuracy. However, in the case of Stacking, we observed fluctuations, with accuracy results ranging between 60% and 73%.