## 4 Result and Discussions
### Feature Importance  
Feature importance of the best model (with CNN + GLCM features), as well as the model that has only non-image features, were determined based on the Shapley Additive Explanations (SHAP). SHAP is a method for interpreting ML models, based on a combination of game theory, Shapley values, and some conditional expectations to compute the importance of each feature [69]. Figures 5-8 present explanations of the model with non-image features only. Figure 5 shows the mean importance of each feature, with \"tantennaelev\" having the highest importance. Next to it is \"distance\", as expected, because it is well established that path loss varies with distance. However, \"tantennaelev\" had the highest impact on the model output because studies have shown that apart from distance, the elevation of a position affects path loss at that position due to variations in height difference between the transmitting and receiving antennas. Thus, for a multiple antenna path loss model, it is wise to include the elevation of the transmitting and receiving antenna positions so that their height difference effect is captured. The \"tantennaelev\" had an importance that superseded that of \"distance\" because it was observed by [PERSON] et al. [70] that elevation affects path loss to the extent that a position at a further distance from the transmitting antenna but at a high elevation could have lower path loss values than a position closer to the transmitting antenna but at a low elevation. The third and fourth most important features are distances in latitude and longitude, and then the height of the receiving antenna (hr) had the least importance. Figure 6 shows the detailed explanations of the features, with the position of the dots with respect to the horizontal axis showing SHAP values that determine whether the path loss is high or low, and the color indicating the feature value. Positive SHAP values mean that the feature has a positive impact on path loss and negative means it has a negative impact. The results show that for higher values of \"tantennaelev\", the path loss value is low, but for lower feature values, the output is high, though with a few instances where the output is low at lower values of \"tantennaelev\". This is because the higher the elevation of the transmitting antenna, the wider the signal coverage; therefore, path loss decreases for the distance concerned. Likewise, the lower the elevation, the smaller the coverage, and the higher the path loss values. The fewer points where the low output values were observed might be due to irregularities in elevations of the receiver position as well as clutter. In the case of \"distance\", it is observed that at lower distances, the path loss values are low and high at higher distances. Some minor instances in which the linearity is violated are due to the effect of other features. However, not only does the difference between transmitting and  
Figure 5: Mean feature importances of the model with non-image features only, with “tantennaelev” having the highest importance and “hr” the least. receiving antenna altitude (height + elevation) affect the signal coverage. Other antenna parameters such as azimuth and tilt also affect the signal coverage, but the study of their effects is beyond the scope of this work. Figure 6: SHAP summary plot of the model with non-image features only, showing the variations in feature values with output path loss value. Figure 7: Dependence plot of the feature “tantennaelev” in a model with non-image features only with the feature “distance_x”. Feature “tantennaelev” has the highest interaction index with “distance_x”. The difference in latitude between the transmitting and receiving antenna and that of longitude (distance_x and distance_y) were observed to push path loss values high and moderate, respectively.