## 3 Results and Discussion
### Predictive Modeling of Surface Roughness
LightGBM exhibited relatively lower R\({}^{2}\) values (0.9611 train and 0.9569 test) and higher training RMSE (0.1303), possibly due to its aggressive histogram-based decision tree learning, which, while computationally efficient, may underrepresent subtle local patterns in Ra evolution. RFR, although known for robustness and stability, yielded the lowest predictive performance in this study (test R\({}^{2}\) = 0.9382 and RMSE = 0.1600), suggesting a more limited ability to model intricate micro-patterns associated with surface morphology in high-speed micro-milling. This aligns with findings from [23], which showed that tree-based bagging methods may underperform compared to boosting and neural network approaches in high-precision applications. The added value of these ML models over traditional methods lies in their ability to model nonlinear dependencies without prespecified equations, adapt to multivariate interactions, and scale efficiently with increased data complexity. These characteristics are essential for advancing smart manufacturing systems and real-time quality monitoring frameworks. Furthermore, the trained models--especially CatBoost and KNN--can be directly integrated into computer numerical control (CNC) systems for real-time surface roughness prediction, enabling dynamic parameter adjustments and minimizing post-processing requirements. This has significant implications for improving productivity, reducing scrap rates, and ensuring consistent product quality in precision manufacturing environments. The predictive trends observed are well aligned with the experimental results described earlier. For example, Ra values increased with higher feed rates and deeper cuts, especially at lower spindle speeds--a pattern accurately captured by the CatBoost and KNN models. Moreover, the nonlinear effects of high spindle speeds (above 15,000 rpm) on surface roughness, likely due to tool wear and thermal softening, were effectively modeled by ensemble-based algorithms. These findings are consistent with earlier work [22; 24], which emphasized the suitability of ML models in identifying complex interactions between machining parameters and output characteristics. In conclusion, CatBoost emerged as the most effective model for predicting surface roughness under varying micro-milling parameters, outperforming other ensemble and non-parametric models. The model's capacity for high-fidelity prediction, even with limited data and complex input-output dynamics, provides a novel contribution to the micro-milling literature. These results demonstrate the practical relevance of ML techniques not only for predictive accuracy but also for enhancing process understanding and control--elements critical to the evolution of intelligent machining systems. Future studies could explore the online implementation of these models in closed-loop machining environments, integrating sensor data for adaptive control and fault prediction. This approach aligns with Industry 5.0 objectives, promoting human-machine collaboration and higher customizationin surface integrity management. These findings are consistent with those of numerous recent studies [19; 23; 27], which collectively underscore the growing role of advanced ML techniques in smart manufacturing and surface quality optimization.