## 4 Results and Discussions  
First, the results are presented by applying feature selection techniques (PCA, mRMR, NCA, and DAE). Then, considering the most relevant features, we evaluate the performance of RF, SVM, and MLP in predicting machine failures in terms of accuracy, precision, and recall. In both cases, feature selection and classification, the results obtained by the techniques are compared. Table 3 shows how each feature (variable) contributes to every principal component. The table allows for observing the nine main components, PC1 to PC9, and the variables contributing to each principal component. Product ID contributes 100% to PC1, while air temperature and process temperature contribute 73.4% to PC2, followed by rotational speed at 100% and torque at 98.7%. It means that with just two main components, it is possible to explain the proportion of the total variance of the data, showing that the variables product ID, air temperature, process temperature, rotation speed, and torque are most important to predict machine failure. Although the first two components explain most of the variance (about 98%), the remaining components can provide important information about less prominent patterns in the data. Therefore, presenting all components allows for a more complete and detailed view of the underlying structure of the data. Table 4 presents the feature selection results for PCA, mRMR, NCA, and DEA techniques. The mRMR presents product ID and tool wear as the most relevant variables, while the NCA presents practically the same contribution level to all input variables. On the other hand, PCA presents the five most important variables: product ID, rotational speed, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{8}{c}{**Contribution (in \%)**} \\ \cline{2-10}  & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** & **PC7** & **PC8** & **PC9** \\ \hline Product ID & 100.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ Air Temperature & 0.0 & 73.4 & 100.0 & 2.0 & 3.1 & 100.0 & 11.1 & 42.9 & 7.1 \\ Process Temperature & 0.0 & 73.4 & 98.7 & 2.0 & 3.1 & 95.7 & 11.1 & 47.6 & 7.1 \\ Rotation speed & 0.0 & 100.0 & 74.0 & 0.0 & 53.1 & 13.0 & 100.0 & 0.0 & 7.1 \\ Torque & 0.0 & 98.7 & 74.0 & 0.0 & 53.1 & 13.0 & 100.0 & 0.0 & 7.1 \\ Tool Wear & 0.0 & 2.5 & 3.9 & 100.0 & 6.3 & 0.0 & 5.6 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contribution of each variable to each principal component. torque, air temperature, and process temperature. When comparing the results of the three techniques, only the product ID variable has a similar contribution level using both mRMR (26.56%) and PCA (22.07%), showing that this variable can be used in the prediction model. However, the mRMR also presents a high level of correlation for the variable tool wear (10.95%). The DAE revealed that air temperature, process temperature, product ID, and tool wear are the four most relevant variables for the failure prediction process. As pointed out in [10], feature selection provides better performance for models to predict machine failures, achieving an accuracy of 82.4% using mRMR. Regarding the use of NCA for feature selection, although [11] presents a gain of 5 to 10% in the accuracy of fault classification by applying this technique, in our study, it was not possible to achieve this gain, as this technique presented practically the same level of importance for all features, as can be seen in Table 4.  
It is worth noting that the RF defined the following order of importance of attributes: torque, rotational speed, tool wear, product ID, air temperature, and process temperature. This order is different from those made by the three attribute selection techniques, which prompts further investigation into the effectiveness of the impurity metrics employed by RF to define the importance of attributes to failure classification.