## 4 Data Sets and Experimental Results
Table 4 reports the results obtained for the same data set but with differently performed pre-processing (for details, see papers [24; 25]). The table also shows the number of decision rules generated via the analyzed approaches. The table only omits the results for the rough set rule-based systems with a covering algorithm approach and discretization, as it has very low test-case coverage. The best result is in boldface. As can be seen, models based on decision rules generated with fuzzy sets or rough sets (results shown in Table 4) have a classification accuracy a bit lower than the results generated via neural networks. However, the value of the F-measure is most interesting, as here, we can clearly see a much lower value, which shows that neural networks, especially the RBF network, have a much higher ability to recognize customers who intend to abandon the company's mobile services. Of course, this type of prediction has a key impact in creating the company's strategy. But, on the other hand, models based on decision rules are very interpretable by experts, while in neural networks, there is no information about which conditions are particularly important in the decision by customers to abandon a company. Thus, in fact, the final decision on which model should be chosen and applied in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **F-m.** & _acc_ & **No. Rule** & **Len Avg** \\ \hline FBS Mamdani model & 0.706 & 0.983 & 158 & - \\ FBS Sugeno model & **0.980** & 0.980 & 5 & 8 \\ RSS Exhaustive algorithm & 0.747 & 0.982 & 19021 & 1.2 \\ RSS Genetic algorithm & 0.761 & 0.983 & 18917 & 1.2 \\ RSS Covering algorithm & 0.711 & 0.982 & 14485 & 1 \\ RSS LEM2 algorithm & 0.944 & **0.997** & 1599 & 4.5 \\ RSS Exhaustive algorithm with discretization & 0.887 & 0.993 & 2034 & 2.9 \\ RSS Genetic algorithm with discretization & 0.891 & 0.993 & 2063 & 2.9 \\ RSS LEM2 algorithm with discretization & 0.933 & **0.997** & 256 & 5.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of F-m. and _acc_ obtained for the fuzzy-based systems (FBSs) [25] and the rough set rule-based systems (RSSs) [24]. Designations: No. rule—the number of rules generated, Len Avg—the average length of rule premises. The best result is in bold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Prec.** & **Recall** & **F-m.** & _acc_ \\ \hline MLP network with 0.997 & 0.982 & 0.989 & **0.999** \\ RBF network with 64 neurons & 0.981 & 0.981 & 0.981 & 0.981 \\ RBF network with 127 neurons & 0.986 & 0.986 & 0.986 & 0.986 \\ RBF network with 190 neurons & 0.990 & 0.991 & 0.990 & 0.991 \\ RBF network with 254 neurons & 0.992 & 0.992 & 0.992 & 0.992 \\ RBF network with 318 neurons & 0.992 & **0.993** & 0.992 & 0.993 \\ RBF network with 381 neurons & 0.993 & **0.993** & **0.993** & 0.993 \\ RBF network with 444 neurons & 0.993 & **0.993** & **0.993** & 0.993 \\ RBF network with 508 neurons & 0.992 & 0.992 & 0.992 & 0.992 \\ RBF network with 572 neurons & 0.991 & 0.991 & 0.990 & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of Prec., recall, F-m., and _acc_ obtained for the multi-layer perceptrons and radial-basis-function network. The best result is in bold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Prec.** & **Recall** & **F-m.** & _acc_ \\ \hline GRU network with 381 neurons & 0.940 & **0.946** & 0.942 & **0.946** \\ GRU network with 444 neurons & **0.942** & **0.946** & **0.944** & **0.946** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Prec., recall, F-m., and _acc_ obtained for the GRU neural network. The best result is in bold. practice depends on the objectives of the company: whether the quality of the prediction itself is more important or clear rules based on which the prediction is made. Figure 4: ROC curves for the RBF models with 64, 127, and 190 neurons in a hidden layer. Figure 3: ROC curves for the MLP model.