## 4 Discussion
For its part, Random Forest regression effectively combines filter and wrapper methods to select features, providing a detailed analysis of each feature's importance in the model [26]. This not only evaluates the relevance of the features but also organizes them in descending order according to their impact on reducing model impurity. Feature selection in RFR utilizes a low-variance filter, Spearman correlation, and Random Forest regression [25]. The low-variance filter eliminates features with minimal variability, while Spearman correlation measures the monotonic relationship between variables, and RFregression analyzes how each feature contributes to the overall accuracy of the model. This allows the algorithm to manage complex data and provide a detailed assessment of each parameter's importance in the model's performance, thereby ensuring greater accuracy and generalization [25; 26; 27; 28]. While [PERSON] et al. achieved significant improvements with their methods, the approach presented in this article simplifies the selection process by training a Gradient Boosting model with all relevant variables that have a strong correlation with tool wear--as seen in the correlation matrix--while still yielding competitive performance. Compared to neural network models, SVMs often exhibit superior generalization abilities, particularly when training data are limited. [PERSON] et al. [29] introduced a novel method for monitoring tool wear in the micro-milling process using an SVM artificial intelligence model combined with vibration and sound signals. This approach addresses the limitations of traditional methods by integrating multiple types of sensory data, significantly improving accuracy in detecting tool wear. The incorporation of vibration and sound signals resulted in a richer dataset, enabling the SVM model to discern patterns that may be obscured with a single type of signal. Building on this concept, [PERSON] et al. [30] developed a multi-stage SVM to identify tool wear based on selected features, allowing for more precise identification by processing features through multiple analytical stages. This method proved effective in managing the complexity of and variability in wear data. However, the performance of the SVM model is heavily dependent on the selection of penalty factors and Kernel function parameters. In their study, the maximum prediction accuracies for BP neural networks, Bayesian networks, and SVM were 83%, 75%, and 72%, respectively--impressive performances, yet not the most precise. In contrast, our model was trained with the same (and more) variables--vibrations, for instance--and achieved a superior \"accuracy\" by obtaining lower MSE and MAE scores, thus improving the predictive capabilities of previous models. Furthermore, the Gradient Boosting model of this study had an impressive performance given its relative simplicity, as shown by the obtained metrics; hence, there is no immediate need to enhance the model for this task. However, should the decision be made to pursue further improvements, several strategies could be considered to potentially enhance its efficacy. While the proposed models demonstrate promising performance, it is important to acknowledge the limitations associated with the dataset size. The training dataset, although robust, may not fully capture the wide range of variations in machining conditions and tool wear patterns encountered in real-world industrial settings. To mitigate this limitation, we employed K-Fold cross-validation with five splits to rigorously evaluate the generalization capacity of the models. While our current approach used standard K-Fold cross-validation, future research could benefit from implementing stratified K-Fold cross-validation to ensure a more consistent representation of tool wear characteristics across all folds. Future studies should also prioritize expanding the dataset by incorporating additional data points or generating new experimental data to further enhance the models' robustness and applicability to diverse scenarios.