## 4 Discussion and Conclusions  
This study was set out to (1) apply well-known ML algorithms to predict the characteristics of soil and (2) to compare the performance of these models for predicting the soil characteristics/classifications. Four tree-based algorithms, including C5, CHAID, QUEST, and CART; two neural networks algorithms, including ANNMLP and ANNRBF; and two Bayesian networks algorithms, including BAYESIANTAN and BAYESIANMARKOV, were applied to a dataset from a geographical study. The performance of each model was evaluated regarding its overall accuracy, recall, precision, stability, and variable importance.  
Among all models employed, the BAYESIANMARKOV model achieved the highest accuracy in the training phase (100%), while this model achieved the lowest accuracy in testing phase (34.21%). This implied that the BAYESIANMARKOV model lacked stability and it may not be suitable for analyzing the soil classification. The possible reason behind the low accuracy of BAYESIANMARKOV model in testing phase might be insufficient testing data. Typically, the availability of more test data is preferred because it provides a better understanding of how well the models generalize to unseen data. Furthermore, with less testing data, the performance statistic might have greater variance. All in all, it is an extremely challenging task to split data into train and test sets when a small dataset is available. While the BN models can be assumed as robust techniques to reason probabilistically, they involve the  
Figure 14: Importance of variables derived from various models employed.  
encoding of possibilities, speculating the points where randomized actions can occur and, therefore, require a more heuristic attempt to create.  
The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. In a comparison between the Bayesian Network and DT models, Bayesian Network models work well with small datasets compare to DTs. These models also have a faster processing time. More importantly, they are less susceptible to overfitting compared to DTs. On the other hand, DTs are much more flexible and easier to understand compared to the Bayesian network models. However, DTs are extremely prone to overfitting. The ANN models outperformed other models in terms of stability. ANN models are now widely used due to their capabilities in generating quick results. However, these models are a black box and suffer from explainability.  
The procedure of modeling presented in this study can be used by researchers and engineers in order to classify the soils during geophysics investigations. In addition, the same procedure can be implemented for other soil types using similar input parameters.  
Conceptualization, D.J.A.; Data curation, R.S.; Formal analysis, C.S.L.; Supervision, E.T.M.; Writing--review and editing, C.S.L., M.R.M. and D.J.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Authors acknowledge with thanks [PERSON] Director--Geotropik, Centre of Geoengineering, Universiti Teknologi Malaysia, for provision of data of this study and encouragement given thorough out the paper.  
The authors confirm that this article content has no conflict of interest.