## 4 Model Performance Result  
In this section, the comparison between the models is presented using the four metrics: accuracy, precision, recall, and the F1 score. This is done at the considered varying training sets (70, 80, and 90) so as to determine the best data splitting that best depicts the data. Table 6 displays the model performance comparison for the two models.  
The performance metrics for the two models under consideration are shown in Table 6 above. The outcome demonstrates that the XGBoost model produced accuracy, precision, recall, and F1 score values of 77.78%, 80.32%, 92.59%, and 86.02% at 70% training set; 77.83%, 80.40%, 92.63%, and 86.87% at 80% training set; and 77.90%, 89.61%, 93.98%, and 87.20% at 90% training set, respectively. For the outcome using the ANN model, the accuracy, precision, recall, and F1 score values are 76.39%, 78.59%, 92.90%, and 85.15% at 70% training set; 76.93%, 79%, 92.95%, and 85.71% at 80% training set; and 77.43%, 79.49%, 93.20%, and 86.37% at 90% training set, respectively. This demonstrates the efficacy of these models in estimating consumer preference for mobile networks.  
ML is therefore recommended for its efficacy in modeling consumer preferences. Compared to the traditional linear models used in consumer choice theory, ML models can capture complex non-linear relationships in consumer preferences. By taking into account the subtle interactions between features, the model can yield more correct predictions of consumer behavior. Since ML algorithms can easily handle large volumes of data, big data sources can be leveraged to obtain more comprehensive and data-driven insights into consumer behavior. ML approaches permit flexible model specification which makes it possible for researchers to model consumer preferences in innovative ways. It allows for the inclusion of diverse preferences and behaviors and does not make strict assumptions about utility functions or decision-making processes. Behavioral biases like loss aversion and framing effects can be incorporated into ML models to capture more realistic decision  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**XGBoost**} & \multicolumn{3}{c}{**ANN**} \\ \hline \hline  & \multicolumn{6}{c}{**Training Sets**} \\ \hline
**Metrics** & **70\%** & **80\%** & **90\%** & **70\%** & **80\%** & **90\%** \\ \hline Accuracy & 0.7778 & 0.7783 & 0.7790 & 0.7639 & 0.7693 & 0.7743 \\ Precision & 0.8032 & 0.8040 & 0.8961 & 0.7859 & 0.7900 & 0.7949 \\ Recall & 0.9259 & 0.9263 & 0.9398 & 0.9290 & 0.9295 & 0.9320 \\ F1 score & 0.8602 & 0.8687 & 0.8720 & 0.8515 & 0.8571 & 0.8637 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model performance.  
Figure 6: AUC plot for ANN model at 90% training set.  
making scenarios. The incorporation bridges the gap between theoretical concepts and empirical findings, consequently enhancing consumer choice theory and leading to more improved and robust models of consumer behavior. One drawback of the ML models is that predictive accuracy is prioritized over interpretability. Because of this black-box nature, it could be necessary to develop new approaches (such feature importance analysis and model-agnostic interpretation methods) extract insights and to validate model outputs.