## 5 Results and Discussion
Lastly, the MLP-ANN model (see Figure 5g) demonstrates a satisfactory predictive performance with a majority of points closely following the ideal line. Nonetheless, there is a slight tendency for higher variability at the extremes of the UCS range, suggesting that while MLP-ANN captures the overall trend effectively, it may struggle with extreme values. Figure 5: Comparison between the scatter plot of each model output versus the actual UCS values. (**a**) Linear Regression, (**b**) Random Forest, (**c**) Gradient Boosting, (**d**) XGBoost, (**e**) LightGBM, (**f**) SVM-SVR, and (**g**) MLP-ANN. Figure 6 illustrates the relative importance of input features for various machine learning models. Understanding feature importance is essential for interpreting model behaviour and identifying key drivers of predictions. Figure 6a highlights the feature RHOB as the most influential predictor, followed by NPHI and DT in the Linear Regression model. The radar plot indicates a significant reliance on RHOB, with other features playing relatively minor roles. This suggests that in the linear model, RHOB holds a dominant explanatory power, likely due to its strong linear relationship with the target variable. Figure 6b shows a similar trend, with RHOB again being the most important feature for the Random Forest model. However, the spread of importance is slightly more balanced, with DT and NPHI also contributing significantly. This indicates that the Random Forest model captures more complex interactions among features compared to Linear Regression. Figure 6c,d both display a notable emphasis on RHOB, but with a more pronounced role for NPHI and DT for the Gradient Boosting and XGBoost models. The radar plots for these models reveal a more distributed importance among the features, suggesting that the boosting methods are effective in leveraging multiple features to enhance predictive accuracy. Figure 6e shows a more balanced distribution of feature importance, with WOB, RHOB, and NPHI all contributing significantly to the LightGBM model. This model's radar plot is more uniform compared to others, indicating that LightGBM utilizes a diverse set of features to make predictions, potentially leading to better generalization. Figure 6f provides a bar chart of feature importances based on absolute coefficients. Here, NPHI emerges as the most influential feature, followed by DT and RHOB for the SVM-SVR model. This distribution reflects the model's ability to capture complex, non-linear relationships where multiple features significantly impact the outcome. Figure 6g uses permutation importance to measure feature relevance. NPHI and DT show the highest importance, indicating their critical role in the neural network's predictions. The reliance on these features suggests that the MLP-ANN model effectively captures intricate patterns in the data. Figure 7 illustrates the Taylor plots for seven different models. As shown in Figure 7a, in the Linear Regression model, the correlation coefficient is moderate, suggesting a reasonable but not exceptional agreement between predicted and observed UCS values. The model's standard deviation is lower than that of the observations, indicating that Linear Regression underestimates the variability in UCS values. The Random Forest model (see Figure 7b) shows a higher correlation coefficient compared to Linear Regression, indicating a stronger relationship between predictions and actual values. The standard deviation is closer to that of the observations, suggesting that Random Forest better captures the variability in UCS values. Figure 7c demonstrates an even higher correlation coefficient, nearing 1.0, for the Gradient Boosting model, which implies a very strong agreement between the model predictions and the actual UCS values. The standard deviation of the predictions aligns closely with that of the observations, indicating that Gradient Boosting effectively captures the variability in the data.