# Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models +
## 4 Experiments with Tabular Data
### Comparisons to Other Classification Problems  
We conducted experiments based on ten repetitions of 10-fold cross validation trials. Table 3 illustrates the results for the \"Breast Cancer\" dataset, with the last four rows showing other methods. The highest average prediction accuracy of the rules (Acc. R. a) was provided by the DIMLPs (97.3%), with QSVMs close behind (97.2% and 97.1%). Thus, FidexGlo has been shown to provide accurate rulesets in comparison with other techniques. On average, more rules were produced by RFs and GBs of depth three than DIMLPs and QSVMs. Furthermore, the average fidelity of the rulesets generated by our models was over 99%. Finally, the lowest average number of rules was obtained by RuleCOSI with an average of 10.8 rules, but the number of antecedents per rule is not reported by the referenced work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Acc. ** & **Fid. ** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant. ** \\ \hline DIMLP (\(h_{2}=0\)) & 97.0 (0.1) & 99.4 (0.2) & 96.9 (0.2) & 97.2 (0.1) & 14.2 (0.4) & **2.2** (0.0) \\ DIMLP (\(h_{2}=10\)) & 97.2 (0.1) & 99.4 (0.2) & **97.3** (0.3) & 97.5 (0.2) & 14.3 (0.8) & **2.2** (0.0) \\ \hline QSVM (lin.) & 97.1 (0.1) & **99.5** (0.1) & 97.2 (0.2) & 97.4 (0.2) & 14.8 (0.5) & **2.2** (0.0) \\ QSVM (RBF) & 97.1 (0.2) & **99.5** (0.3) & 97.1 (0.3) & 97.3 (0.2) & 14.2 (0.6) & 2.3 (0.0) \\ \hline RF (\(d=10\)) & 97.1 (0.3) & 99.3 (0.2) & 96.8 (0.3) & 97.3 (0.4) & 29  
Table 4 shows the results for the \"Divorce\" classification problem, with the last two rows representing other methods. The highest average prediction accuracy of the rules was obtained again by the DIMLPs with 98.0%, but all our models were very close. Furthermore, the lowest average number of generated rules was provided by QSVMs with the RBF kernel (2.4). Again, the average fidelity of the rulesets generated by our models was over 99%. In Table 5, we present the results for the \"Ionosphere\" classification problem, with the last four rows showing other methods. The highest average prediction accuracy of the rules was provided by RFs with 94.3%, but with more generated rules than RuleFit (28.8 versus 25). Again, FidexGlo has been shown to provide accurate rulesets in comparison with other techniques in the state of the art. The lowest average number of generated rules was provided by GB, with 13.8 rules on average. Finally, except for QSVM using the linear kernel, the average fidelity was over 97%. For this classification problem with 34 variables, we wondered how the complexity of the rules varies as a function of the Drop-Var and Drop-Hyp parameters for DIMLP ensembles trained by bagging. Figure 5 shows the variation of several characteristics of the rules as a function of the dropout parameters (\(p\) and \(q\)) with the same values varying from 0 to 0.99 (last bar). Each bar is the result of a cross-validation. We observe that the average number of rules is stable up to 0.5 on the x-axis and then starts to decrease. Finally, there is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Acc. ** & **Fid. ** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant. ** \\ \hline DIMLP (\(h_{2}=0\)) & **98.2** (0.2) & 99.8 (0.3) & **98.0** (0.3) & **98.2** (0.2) & 6.6 (0.4) & 1.2 (0.0) \\ DIML (\(h_{2}=10\)) & **98.2** (0.0) & 99.6 (0.4) & 97.8 (0.0) & **98.2** (0.0) & 6.8 (0.3) & 1.2 (0.0) \\ \hline QSVM (lin.) & 97.6 (0.0) & 99.8 (0.3) & 97.8 (0.3) & 97.8 (0.3) & 6.0 (0.3) & 1.2 (0.0) \\ QSVM (RBF) & 97.6 (0.0) & 99.5 (0.5) & 97.7 (0.3) & 97.9 (0.3) & **2.4** (0.2) & **1.0** (0.0) \\ \hline RF (\(d=10\)) & 97.6 (0.0) & 99.8 (0.3) & 97.8 (0.3) & 97.8 (0.3) & 6.9 (0.3) & 1.2 (0.0) \\ RF (—) & 97.6 (0.0) & **99.9** (0.2) & 97.7 (0.2)a further increase from the penultimate bar. The top right shows the average number of antecedents per rule on the vertical axis.