## 4 Results and Discussions
Table 5 shows the results obtained by RF, SVM, and MLP models on training and test data, accompanied by the training time for each model. In the case of RF considering feature selection, we observed good performance in the training and test data, maintaining high accuracy, precision, and recall rates, which were 1.0 for the three metrics in the training and 0.98, 0.97, and 0.98 for the test data. The SVM performed well, but it required extensive training time compared to RF and MLP, and despite the MLP demonstrating robustness in training and testing data, RF proved to be more efficient. Without the application of feature selection, RF was more efficient than the other two classifiers, but with lower rates for the three evaluation metrics in the tests. The SVM showed promising results but with a significantly longer training time, and the MLP preserved solid performance. Overall, the models obtained promising training and testing data results without using strategies to mitigate overfitting, such as cross-validation and regularization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **PCA** & **mRMR** & **NCA** & **DAE** \\ \hline Product ID & 22.07 & 26.56 & 12.50 & 11.33 \\ Air temperature & 16.20 & 1.27 & 12.40 & 12.41 \\ Process temperature & 16.20 & 0.78 & 12.40 & 12.33 \\ Rotational speed & 22.07 & 6.68 & 12.30 & 10.78 \\ Torque & 21.78 & 7.22 & 12.70 & 10.03 \\ Tool wear & 0.55 & 10.95 & 12.90 & 11.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Importance of features according to each feature selection technique. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Train Data**} & \multicolumn{3}{c}{**Test Data**} & \multicolumn{3}{c}{**Training**} & \multicolumn{1}{c}{**Feature**} \\  & **Accuracy** & **Precision** & **Recall** & **Accuracy** & **Precision** & **Recall** & & \\ \hline RF & 1.0 & 1.0 & 1.0 & 0.98 & 0.97 & 0.98 & 25 & \\ SVM & 0.97 & 0.96 & 0.97 & 0.98 & 0.96 & 0.98 & 331 & Yes \\ MLP & 0.98 & 0.96 & 0.98 & 0.98 & 0.97 & 0.98 & 2 & \\ \hline RF & 0.98 & 0.9  
In Table 6, precision and recall metrics are provided for different failures predicted by the three models analyzed. It is noteworthy that the RF model demonstrates solid performance in failure prediction, especially for heat dissipation failure (85%), no failure (98%), power failure (86%), and overstrain failure (100%), indicating its high performance in identifying these types of failure. In contrast, the SVM model exhibits varied performance for different failure categories, presenting good accuracy for no failure (100%), power failure (81%), and heat dissipation failure (81%), but facing difficulties in detecting failures overstrain, random, and toolwear, for which a rate of 0% was obtained for precision and recall. The MLP model stands out for its solid performance. It achieved a precision of 98% for no failure, 96% for power failure, and 100% for heat dissipation failure. However, this model also faces challenges in classifying overstrain, random, and toolwear failures. Thus, the RF model shows superior overall performance. It obtains high accuracy, precision, and recall for four failure types. However, it failed to classify the random and toolwear failures. Given these results, the importance of adopting a comprehensive approach when choosing feature selection techniques to analyze datasets is evident, as exemplified by the study of [28] and the results presented in Tables 4 and 5. The careful choice of feature selection technique directly impacts the selection of the most relevant variables, reflecting the model's accuracy as in [10; 11] to better support decision-making aimed at predictive maintenance. Regarding the overall performance of the techniques shown in Tables 4 and 5, RF presented more consistent results in precision and recall than the other techniques in several failure categories, especially in heat dissipation and power failure, in which RF achieved a precision of 0.85 and 0.86, respectively, and a recall of 0.87 and 0.81.