## 5 Results and Discussion  
The visual representations in Figures 3 and 4 offer a comprehensive overview of the comparative performance of various machine learning models in predicting UCS values for both the training and testing datasets. These figures serve as valuable tools for assessing the predictive accuracy and generalization capabilities of different models in the context of geotechnical engineering applications. Figure 3 presents a comparative analysis of predicted and actual UCS values against the data index for a training dataset using different machine learning models. In the Linear Regression model (Figure 3a), the predicted UCS values (red line) demonstrate a general alignment with the actual values (blue dots), but significant deviations are evident, particularly in regions where the actual values exhibit sharp fluctuations. These deviations suggest that the Linear Regression model struggles to capture the non-linear patterns in the data accurately. Random Forest (see Figure 3b) and Gradient Boosting (see Figure 3c) exhibit closer alignment with the actual UCS values compared to Linear Regression. Both models reduce the magnitude of the deviations, reflecting their capability to handle non-linearities better than a simple linear model. Among these, Gradient Boosting appears to have a slight edge, maintaining a tighter fit throughout the dataset. XGBoost (see Figure 3d) further improves the accuracy, displaying a strong correlation between the predicted and actual values across the entire data index. The model's robustness in managing diverse data patterns is evident from the minimal deviations observed. This is reflected in the stability of its predictions, even when faced with variations in the underlying data characteristics,  
such as different geological formations or variations in drilling parameters. The minimal deviations observed in the model's predictions indicate that it can effectively generalize from the training data to unseen samples, thereby minimizing the risk of overfitting. This robustness ensures that the model remains reliable under various operational conditions, making it a valuable tool for practical applications in the field. LightGBM (see Figure 3e) demonstrates a performance comparable to XGBoost, with a similarly tight fit and minimal errors. Both boosting algorithms, XGBoost and LightGBM, seem to offer superior predictive accuracy due to their advanced ensemble learning techniques. The SVM-SVR model (see Figure 3f) shows a reasonable fit but with more pronounced deviations in certain sections of the data index. This suggests that while SVM-SVR is effective, it might not be as versatile as the boosting methods in capturing the full complexity of the UCS values. Finally, the MLP-ANN model (see Figure 3g) provides a fit comparable to the boosting models, with predictions closely following the actual values. The neural network's ability to model intricate patterns in the data contributes to its high predictive performance. Figure 4 provides a comparative analysis of several predictive models in estimating UCS against actual values using a testing dataset. Linear Regression (Figure 4a) reveals substantial discrepancies between predicted and actual UCS values, which are particularly notable in several spikes and troughs where the model fails to capture the variability in the data. This reinforces the earlier observation that Linear Regression is less effective in modelling complex, non-linear relationships inherent in the dataset. As depicted in Figure 4b, Random Forest shows improved performance compared to Linear Regression, with predictions more closely aligned with actual values. However, some deviations persist,  
Figure 3: Comparison between the predicted values of each model and the actual UCS values versus the data index for the training dataset. (**a**) Linear Regression, (**b**) Random Forest, (**c**) Gradient Boosting, (**d**) XGBoost, (**e**) LightGBM, (**f**) SVM-SVR, and (**g**) MLP-ANN.