# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 5 Discussion  
Feature importance was used as a means to explain the different propagation scenarios that arise when the transmitter is placed at different heights. For both heights, the same set of three features captured more than 75% of the total importance, leaving the remaining 25% (or less) for the remaining 20 features. It is worth mentioning that this behavior is the same according to both machine learning models.  
Comparing the inner rankings among the three most important features, it is clear that _Buildings_ outperforms the other features when the transmitter is placed at 30 m. The situation clearly changes when elevating the transmitter to a height of 35 m, in which case \(LOS\_3b\) becomes the most important feature for this scenario.  
It is therefore straightforward to conclude that when the transmitter is placed at 35 m, the mechanism of over-rooftop diffraction has a large influence, because this mechanism is connected with the feature \(LOS\_3b\). By comparison, the mechanism of multiple reflections has a stronger influence when the transmitter is placed at 30 m, as concluded through its connection with the feature _Buildings_.  
A closer look at Figure 4; Figure 5 helps explain why the prediction for the 30 m case is worse than that for the 35 m case: for the 35 m case, the dominance of \(LOS\_3b\) is much clearer than the dominance of _Buildings_ for the 30 m case. That is, over-rooftop diffraction is clearly greater in the 35 m case, whereas the propagation profile of the 30 m case cannot be attributed with the same level of clarity to a single mechanism. This sharper propagation profile makes prediction for the 35 m case easier for the machine learning models, in contrast to the 30 m case, in which the existence of different mechanisms makes the simulation more challenging.  
This can also be observed in Figure 6: the error curve drops at a much faster pace in the 35 m case, meaning that a small number of features contains a significant amount of the information needed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model No** & **MAE (dB)** & **Trees** & **Features** & **Training Time (s)** & **Response Time (ms)** \\ \hline
1 & 3.54 & 700 & 23 & 68.34 & 382.51 \\ \hline
2 & 3.54 & 700 & 18 & 58.46 & 354.79 \\ \hline
3 & 3.54 & **300** & **18** & **32.02** & **147.88** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time comparison of XGBoost models with the same MAE, for the 30 m case. (Data in bold font indicates the smaller values.)  
Figure 7: The XGBoost model obtains the minimum MAE value with a smaller number of trees for an optimum input subset of 18 features (for the 30 m case).  
to model the propagation scenario. However, in the 30 m case the improvement takes place more smoothly, as indicated by the small drops in the prediction error. This is due to the co-existence of different propagation mechanisms for the particular transmitter height.  
Figure 7 and Table 3 demonstrate the means of benefiting by eliminating poorly contributing features: less complex models can provide the same prediction performance more quickly, thus making real-time predictions more feasible.