## 3 Results and Discussion
It has almost the same R\({}^{2}\) value for all turbulence classes and outputs. The decision tree model also shows stability but with slightly less prediction accuracy. The other models show variations among the same output for different turbulence classes and vice versa. This indicates that the random forest model is the most suitable one to test for generalization. To achieve this, a random forest model is retrained on 70% of the data from a single dataset corresponding to only one turbulence class. The trained model is then tested to predict the outputs of the other two turbulence classes. This process is repeated thrice per output, once for each turbulence class. The results are shown for the blade tip deflections in the flapwise and edgewise directions. Figure 7 shows the random forest generalization results for the blade tip flapwise deflection. Figure 5: Mean R\({}^{2}\) for all outputs with different turbulence classes. Figure 6: Mean R\({}^{2}\) for all turbulence classes with different outputs. On the diagonal, the prediction results are shown for the random forest model on the turbulence class on which it has been trained. The accuracies of the models are above 98%, which is satisfactory and shows the high performance of random forests. Off diagonal, the model predicts the turbulence classes for which it has not been trained. The accuracies range between 79% and 85%, which is not entirely effective. However, it can show good preliminary results in predicting the flap's deflections under any inflow condition. It is also noteworthy that the model trained on turbulence class B shows equally accurate predictions for turbulence classes A and C. This is because class B represents medium turbulence, so it is halfway between the higher turbulence A and the lower turbulence C. This leads to the recommendation of training the model on the intermediate flow conditions in order to generalize it to other flow conditions. The results for the model trained on class B turbulence are plotted against time to observe the model's performance with other turbulence classes. Also, the normality of  
Figure 7: Random forest generalization results—Actual vs. predicted blade tip flapwise deflections. the residuals, which is a key quality metric, is checked for that model. The time marching actual and predicted deflections of the blade tip in the flapwise direction and the residual distributions are shown only for the model trained on class B in Figure 8. The results of the models trained on classes A and C are shown in Appendix A. The predicted deflections follow the actual ones almost perfectly for the turbulence class B on which it has been trained. This is also represented in the normality of residuals; most of the residuals are distributed around the zero value, which indicates that the prediction errors are minimal. For the other two turbulence classes, the model can still follow the deflection trend and predict the output with satisfactory accuracy. The predictions in little occurrences under or overestimate the deflections; however, these results can be used for preliminary analyses. Figure 8: Time-marching flapwise deflections and residuals distribution for the random forest model trained on turbulence class B. The process is repeated for edgewise deflections. The model is trained on each turbulence class and is used to predict its outputs and the outputs of the other two classes. The predicted values are plotted against the actual outputs and are shown in Figure 9.  
In the edgewise direction, the model performs with higher accuracy. The accuracy of predictions is above 94%, even for the datasets the model has not been trained on. This high accuracy gives more confidence in generalizing the random forest model in the edgewise structural dynamics outputs. A model trained on a single dataset can be used with high confidence for any inflow conditions. To further examine the accuracy, the time marching deflections are plotted again for turbulence class B, but in the edgewise direction.