# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Qualitative Comparison of Heatmaps from LRP, SHAP, and LIME  
One of the main ideas of our work was to show that LRP can perform well for explainability of the deep model in various forms. SHAP and LIME are two other common techniques used for explainability. Using LRP as the baseline explainability technique, we demonstrate advantages of LRP because it highlights the same features as discriminative as those of SHAP and LIME but in far less execution time.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model Name** & **Train/Test** & **Acc** & **Prec** & **Recall** & **Spec** & **F1-Score** & **Cross-V** \\ \hline
**M-1D-CNN-1-31 \({}^{*}\)** & **80/20** & **0.9991** & **0.6732** & **0.9520** & **0.9992** & **0.7866** & **Yes** \\ \hline M-1D-CNN-2-31 \({}^{*}\) & 80/20 & 0.9989 & 0.6507 & 0.8779 & 0.9992 & 0.7446 & Yes \\ \hline M-1D-CNN-3-31 \({}^{*}\) & 80/20 & 0.9987 & 0.6079 & 0.8553 & 0.9990 & 0.7037 & Yes \\ \hline Logistic Regression-31 \({}^{*}\) & 80/20 & 0.9229 & 0.0201 & 0.9044 & 0.9230 & 0.0393 & Yes \\ \hline Decision Tree-31 \({}^{*}\) & 80/20 & 0.9651 & 0.0442 & 0.8762 & 0.9652 & 0.0840 & Yes \\ \hline Random Forest-31 \({}^{*}\) & 80/20 & 0.9946 & 0.2232 & 0.8579 & 0.9948 & 0.3533 & Yes \\ \hline Gaussian NB-31 \({}^{*}\) & 80/20 & 0.9748 & 0.0550 & 0.8474 & 0.9751 & 0.1033 & Yes \\ \hline Logistic Regression [57] & 80/20 & 0.81 & 0.76 & 0.9 & – & 0.82 & No \\ \hline Isolation Forest [59] & 70/30 & 0.997 & – & – & – & 0.63 & No \\ \hline Local Outlier Forest [59] & 70/30 & 0.996 & – & – & – & 0.51 & No \\ \hline SVM [59] & 70/30 & 0.7009 & – & – & – & 0.41 & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our 1D-CNN (with \({}^{*}\)) performance on CCFDD against published results.  
Figure 5: Visualising LRP heatmaps for local (individual) and global (all) TP and TN samples in the CCFDD testing set. Feature sequence of (**a,c**) is similar to (**b,d**).  
Table 5 and 6 show the features ranked in descending order for LRP, SHAP, and LIME from the model trained on TCCPD and CCFDD, respectively. The ranking is done based on the approach explained in Section 3.3. The value 28 shows that the feature is highly discriminative and is considered important whereas 1 means having low importance in the tables. The features are sorted in descending order of LRP ranking, whilst showing the other rankings of LIME and SHAP with respect to the LRP ranking. As we have taken top and bottom eight features from TP and TN each, therefore, if we consider the important (top) features in the first row of Table 5, and similarly for corresponding SHAP and LIME, we can see that the features ranked high (e.g., with 28, 27) in LRP are mostly ranked high in SHAP and LIME as well (e.g., Contract_M_M, PhoneService, Tenure, IS_Fiber_Optic, Monthly charges). Five out of eight features are common for all three. In addition, the time taken by LRP running on CPU (MacBook Pro with Intel Core i5, 2.3 GHz 1 Processor with 2 Cores, and 16 GB RAM) is 1-2 s to generate a heatmap for a single record at test time which is far lower than the time taken by LIME (22 s) and SHAP (108 s). This shows that LRP is faster. We also show in the section that it selects a highly discriminative feature set, that if used with a simple classifier, will generate a similar or better performance. This is possible because the ambiguous or redundant features are removed which were confusing the system. The SHAP and LIME code can be slightly optimised by either changing some of its parameters (for example the size of the neighbourhood (we used default of 10, if we increase it increases the time it takes), parameters for regularisation) however, it takes more time compared to LRP.