## 3 Results
### Proactive System Based on Predictions
#### 3.2.1 Prediction Models Applying Classical Machine Learning Methods  
Once we conducted our research using classical machine learning methods, the following results were obtained. To facilitate readability, we have divided the results according to the classification area and then by the selected target. Figure 9: Examples of Sentinel data distribution deployment over the investment casting plant. Primary Coating Area Predictions  
Regarding the results achieved by traditional classifiers using primary coating data to predict the elongation of the castings (see Table 1), it can be observed that the highest accuracy value was obtained by applying a kNN classifier with the value of \(k=2\). This classifier was able to achieve a fairly high level of accuracy of approximately 80%. However, the datasets for the learning phase could be enhanced. The rest of the classifiers provided good results, as most of them remain above 75% accuracy. In contrast, surprisingly, one of the SVMs, the one taught with the RBF kernel, did not exceed 60% accuracy. On the other hand, the Bayesian networks performed well, even improving the AUC value, which shows that the management between false positives and false negatives could be better when we use this algorithm. Then, when the same data were used to predict the behavior of castings in terms of pore formation, an accuracy of 76% was achieved using the kNN algorithm. The optimal \(k\) value that gave the best results was \(k=1\). It seems that this defect is more difficult to foresee due to the majority of the results not exceeding 70% accuracy. Only one classifier, an artificial neural network, achieved results comparable to the kNN. Perhaps, applying a different type of network and increasing the number of epochs in the learning phase could improve performance. Table 2 summarizes the obtained results, including error rates, to facilitate comparisons between classifiers. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Bayes network (K2) & 77.48 & 0.26 & 0.41 & 0.81 \\ Bayes network (hill climber) & 77.48 & 0.26 & 0.41 & 0.81 \\ Bayes network (TAN) & 76.00 & 0.35 & 0.43 & 0.76 \\ \hline Naive Bayes & 76.74 & 0.27 & 0.41 & 0.80 \\ \hline Artificial neural network MLP & 71.64 & 0.34 & 0.48 & 0.70 \\ \hline Support vector machines (polynomial kernel) & 77.36 & 0.23 & 0.43 & 0.77 \\ Support vector machines (normalized polynomial kernel) & 74.05 & 0.26 & 0.48 & 0.74 \\ Support vector machines (Pearson VII) & 74.26 & 0.26 & 0.47 & 0.74 \\ Support vector machines (radial basis function) & 59.33 & 0.41 & 0.63 & 0.63 \\ \hline Voted perceptron & 73.79 & 0.26 & 0.47 & 0.78 \\ \hline K-nearest neighbors (K = 1) & 74.55 & 0.34 & 0.47 & 0.71 \\ \hline K-nearest neighbors (K = 2) & 79.71 & 0.32 & 0.41 & 0.79 \\ K-nearest neighbors (K = 3) & 77.60 & 0.36 & 0.43 & 0.75 \\ K-nearest neighbors (K = 4) & 77.74 & 0.36 & 0.43 & 0.75 \\ K-nearest neighbors (K = 5) & 76.86 & 0.36 & 0.43 & 0.76 \\ K-nearest neighbors (K = 6) & 76.86 & 0.36 & 0.43 & 0.76 \\ \hline C4.5 & 66.50 & 0.42 & 0.48 & 0.67 \\ \hline Random forest (num. iterations = 50) & 75.50 & 0.34 & 0.44 & 0.73 \\ Random forest (num. iterations = 100) & 75.31 & 0.34 & 0.44 & 0.74 \\ Random forest (num. iterations = 150) & 75.17 & 0.34 & 0.44 & 0.73 \\ Random forest (num. iterations = 200) & 75.17 & 0.34 & 0.44 & 0.73 \\ Random forest (num. iterations = 250) & 75.50 & 0.34 & 0.44 & 0.73 \\ Random forest (num. iterations = 300) & 75.64 & 0.34 & 0.44 & 0.74 \\ Random forest (num. iterations = 350) & 75.31 & 0.34 & 0.44 & 0.74 \\ \hline \end{tabular}
\end{table} 
Table 1: Results predicting the mechanical property elongation when we applied classical machine learning methods using data extracted only from the primary coating area. The best classifier, marked in gray, was the kNN with \(k=2\), with an accuracy level (percentage of correctly classified instances) close to 80%.