## 3 Results
### Model Selection Results
The standard deviation for XGBoost was extremely low (0.00093), reflecting high stability in predictions. Confidence intervals were exceptionally narrow, such as [0.9488, 0.9492], making XGBoost the most consistent and accurate model for these dataset. LightGBM also demonstrated excellent performance, with the best configuration achieving a mean \(R^{2}\) of 0.9474. This configuration used 100 leaves, 500 estimators, a learning rate of 0.2, and a maximum depth of 20. Other configurations, such as reducing the depth to 10 or lowering the learning rate to 0.1, resulted in slightly lower RMSE values of 902.16747 and \(R^{2}\) values of 0.9425. The standard deviation for LightGBM was slightly higher than XGBoost but still low (0.0011 to 0.0012). Confidence intervals remained narrow across all configurations, confirming stable performance. LightGBM provides an excellent balance between accuracy and computational efficiency. The best Support Vector Machine model (linear kernel, regularisation parameter \(C=1.0\), \(\epsilon=0.01\)) achieved a mean RMSE of 1949.72586 and a mean \(R^{2}\) of 0.7544. While this performance is lower compared to ensemble methods, it demonstrates moderate predictive accuracy. The standard deviation was low (0.0017) and the confidence interval was narrow ([0.7541, 0.7547]), indicating stable but less competitive performance compared to other models. Among all models (see Table 5 and Figure 3), XGBoost emerged as the top performer, achieving the highest mean \(R^{2}\) score of 0.9490, indicating excellent predictive power and minimal variance across runs and demonstrating excellent stability with minimal variance. LightGBM followed closely with a mean \(R^{2}\) of 0.9474, demonstrating robust performance, making it another highly reliable choice. Decision Trees and Random Forests achieved strong scores as well (0.8909 and 0.8728, respectively), showcasing their effectiveness despite their lower complexity. Neural networks showed good performance with mean \(R^{2}\) values exceeding 0.81; however, they exhibited higher variance, indicating less stability compared to ensemble methods. Finally, \(K\)-NN and SVM models exhibited moderate predictive capabilities, with mean \(R^{2}\) values of 0.8081 and 0.7544, respectively. Overall, ensemble methods such as XGBoost and LightGBM demonstrated the best trade-off between accuracy, stability, and reliability, and emerged as the most reliable and accurate for shaft power estimation. Each bar in Figure 4 represents the mean \(R^{2}\) value achieved by a different machine learning model, providing a visual comparison of their predictive performance. The red dashed line indicates that the mean baseline value of \(R^{2}\) is 90.2 and it is almost equal to 1500 for the RMSE, serving as a reference point to easily identify models that perform above or below this threshold. Additionally, the exact \(R^{2}\) values are labelled above each bar to facilitate a quick and precise comparison between models. The baseline mean \(R^{2}\) value is 0.9028. Based on the model performances, we recorded the following:  
* **k-NN** achieved a mean \(R^{2}\) of 0.8081, which is lower than the baseline. * **Random Forest** achieved a mean \(R^{2}\) of 0.8728, which is lower than the baseline. * **Neural networks** achieved a mean \(R^{2}\) of 0.8193, which is lower than the baseline. * **Decision Tree** achieved a mean \(R^{2}\) of 0.8909, which is slightly lower than the baseline. * **XGBoost** achieved a mean \(R^{2}\) of 0.9490, which is higher than the baseline, indicating better performance. * **LightGBM** achieved a mean \(R^{2}\) of 0.9474, which is also higher than the baseline, indicating better performance. * **SVM** achieved a mean \(R^{2}\) of 0.7544, which is lower than the baseline. When evaluating models based on the Root Mean Squared Error (RMSE), the following insights emerged:  
* **XGBoost** achieved the lowest RMSE of 888.30, indicating the highest accuracy in absolute prediction error.