To evaluate the performance of our models, we chose balanced accuracy, precision and F1 score as key metrics because they provide a balanced assessment in contexts where class imbalance can significantly affect model performance. We implemented cross-validation using seven repeated partitions of the data, a process performed using the Optuna library, which allowed us to effectively optimize the hyperparameters while evaluating the robustness of the model under different data configurations. This process allows the selection of the best model focused on a comprehensive evaluation of the performance of different hyperparameter configurations using established metrics. To optimize the selection, we not only considered the maximum accuracy of the models, but also applied a Pareto optimization approach. This approach allowed us to balance two important criteria: minimizing the discrepancy between training and validation results, and maximizing performance based on our evaluation metrics. By implementing this Pareto model , we were able to identify those models that offered an ideal balance between avoiding overfitting and ensuring high predictive accuracy. This evaluation method was essential in identifying robust models capable of making accurate and reliable predictions in real-world applications, thus ensuring their applicability and effectiveness in operational environments.