## 3 Results
### Primary and Secondary Coating Area Predictions
This method performed only three percentage points below the best meta-classification method, which is acceptable given the simplification it provides. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Grading (meta: K-nearest neighbors with K = 2) & 70.27 & 0.30 & 0.53 & 0.59 \\ Grading (meta: K-nearest neighbors with K = 3) & 70.27 & 0.30 & 0.53 & 0.59 \\ Grading (meta: K-nearest neighbors with K = 4) & 70.27 & 0.30 & 0.53 & 0.59 \\ Grading (meta: K-nearest neighbors with K = 5) & 70.27 & 0.30 & 0.53 & 0.59 \\ Grading (C4.5) & 69.37 & 0.31 & 0.54 & 0.58 \\ \hline Stacking (meta: naive Bayes) & 50.96 & 0.49 & 0.67 & 0.58 \\ Stacking (Bayes network–TAN) & 68.32 & 0.43 & 0.47 & 0.49 \\ Stacking (meta: K-nearest neighbors with K = 1) & 62.59 & 0.40 & 0.57 & 0.55 \\ Stacking (meta: K-nearest neighbors with K = 2) & 65.55 & 0.42 & 0.54 & 0.54 \\ Stacking (meta: K-nearest neighbors with K = 3) & 62.34 & 0.41 & 0.51 & 0.57 \\ Stacking (meta: K-nearest neighbors with K = 4) & 65.45 & 0.41 & 0.50 & 0.57 \\ Stacking (meta: K-nearest neighbors with K = 5) & 63.25 & 0.43 & 0.50 & 0.56 \\ Stacking (C4.5) & 59.12 & 0.44 & 0.55 & 0.52 \\ \hline Multi-scheme (Bayes network–TAN) & 62.62 & 0.41 & 0.49 & 0.61 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._ \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline By vote (minimum probability) & 48.79 & 0.19 & 0.34 & 0.67 \\ By vote (maximum probability) & 69.74 & 0.41 & 0.45 & 0.80 \\ By vote (average of probabilities) & 73.17 & 0.33 & 0.41 & 0.82 \\ By vote (product of probabilities) & 48.79 & 0.19 & 0.34 & 0.67 \\ By vote (majority voting) & 73.64 & 0.26 & 0.47 & 0.74 \\ \hline Grading (meta: naive Bayes) & 74.55 & 0.25 & 0.46 & 0.75 \\ Grading (Bayes network–TAN) & 74.21 & 0.26 & 0.47 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 1) & 74.07 & 0.26 & 0.46 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 2) & 73.43 & 0.27 & 0.48 & 0.73 \\ Grading (meta: K-nearest neighbors with K = 3) & 73.71 & 0.26 & 0.47 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 4) & 74.02 & 0.26 & 0.47 & 0.74 \\ Grading (meta: K-nearest neighbors with K = 5) & 74.45 & 0.26 & 0.46 & 0.74 \\ Grading (C4.5) & 74.48 & 0.26 & 0.46 & 0.74 \\ \hline \end{tabular}
\end{table} 
Table 11: 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 in addition to melting data. The best meta-classifier, marked in gray, was the stacking method using a TAN Bayesian network for combining the results, with an accuracy level (percentage of correctly classified instances) of close to 77%. Then, the final work focused on meta-classification algorithms using the complete dataset to predict the occurrence of shrinkages. Table 12 summarizes all gathered results. Recalling that the best classical classifier was naive Bayes with a success rate of 70.55%, in this experiment, we obtained values close to 69% when using the by vote method under the maximum probability algorithm. For this prediction use case, all the algorithms performed very similarly. Thus, both simple and complex algorithms demonstrated their ability to perform the prediction task at comparable levels. In fact, the majority voting method produced results nearly identical to those of the best meta-classification method and even the best-performing classification algorithm. Once again, this experiment reinforces that meta-classification could be a viable option for mitigating the challenges associated with classical machine learning algorithms.