# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 4 Results and Discussion
The tree depth value is set to 10, and the model is configured. In this study, non-linear SVR was utilised, specifically testing radial basis function (RBF) and polynomial kernels. The RBF kernel was selected as it provided the most successful results. This kernel assesses the input features of data points using a Gaussian measure to determine the similarity or distance between pairs. The model's parameters included an overlapping penalty value of 200 and an RBF sigma value of 0.5. In this study, the past five time steps were used for time series forecasting. That is, for each forecast, the model considers data from the previous five time periods. The data are reshaped to relate the past time steps of the independent variables (X) to the current values of the dependent variable (y). Table 2 presents a comparison of the models' evaluations using the ANN, RF regression, SVR, and time series methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ANN** & **RF** & **SVR** & **Time Series** \\ \hline R\({}^{2}\) & 0.925 & 0.878 & 0.905 & 0.863 \\ MSE & 0.001 & 0.015 & 0.003 & 0.017 \\ MAE & 0.016 & 0.098 & 0.025 & 0.103 \\ MAPE & 0.043 & 0.137 & 0.078 & 0.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of models. Figure 2: ANN model. Figure 3 shows the R\({}^{2}\) analysis. The R\({}^{2}\) is a statistical measure used to assess a model's performance in regression analysis. It indicates how closely the data align with the regression line. In other words, it shows how much the dependent variable will change, on average, in terms of its own unit of measurement, when the independent variable changes by one unit in its own unit of measurement [59]. The study's findings revealed that the R\({}^{2}\) metric was 92.5% for the ANN, 87.8% for RF regression, 90.5% for SVR, and 86.3% for the time series. The R\({}^{2}\) metric, ranging from 0 to 1, measures the fit quality of a regression model, with values approaching 1 indicating a superior fit. Although the ANN emerged as the most successful model in this analysis, all four models demonstrated efficacy in their predictive capacities. Figure 4 shows the MSE analysis. The MSE serves as a metric to gauge the accuracy of a regression model's predictions compared to actual values. It is determined by finding the average of the squared differences between the predicted and true values. A lower MSE signifies better model performance [60]. This study found that the MSE metric for the ANN was 0.001; for RF regression, it was 0.015; for SVR, it was 0.003; and for time series, it was 0.017. Although the model with the fewest errors in this study was the ANN, it was found that the error rates of all four models were low and acceptable. Figure 5 shows the MAE analysis. The MAE metric calculates the sum of the absolute differences between the predicted values and actual values. Essentially, it quantifies how far away each prediction is from the true value [61]. This study revealed values of 0.016 for the ANN, 0.098 for RF regression, 0.025 for SVR, and 0.025 for the time series. The ideal value of the MAE is zero because it means that the predictions match the actual values exactly. Lower MAE values represent better model performance. This study found that the MAE value in all four models was remarkably close to the ideal one. Figure 6 shows the MAPE analysis. The MAPE metric shows the incorrectness of each prediction as a percentage compared to the true value [60]. This study revealed values of 0.043 for the ANN, 0.137 for RF regression, 0.078 for SVR, and 0.144 for time series. It is widely recognised that the nearer the value of the MAPE is to zero, the more accurate the predictions are. Figure 3: R\({}^{2}\) analysis. This study found that the MAPE metric across all four models closely approached the ideal and acceptable benchmark. Upon analysing the error and success metrics, the models were ranked in terms of performance, with the ANN achieving the highest success and lowest error rates, followed by SVR, RF regression, and subsequently time series.