## 3 Results and Discussions  
### Evaluation of Results of Prediction Techniques  
For each prediction technique, a 10-fold cross-validation using the labeled dataset was repeated 50 times, and the experimental results were evaluated using two performance measures: MAE and RMSE. Table 5 explains the descriptive statistics of the evaluation results of the repeated experiments and shows that SVRmulti was the best among the eight prediction techniques with respect to both performance metrics.  
In addition, Table 6 shows the results of pairwise \(t\) tests between the eight prediction techniques, performed to statistically investigate the effect of using a prediction technique on performance measures. The hypotheses in Table 6 were generated to represent comparisons between different prediction techniques. For example, a hypothesis \"A \(>\) B\" implies that the prediction technique A has a bigger prediction error than B; therefore, B is a better prediction technique.  
The comparison results in Table 6 were interpreted and used to determine whether corresponding hypotheses were supported or not by the repeated experiments in terms of both performance measures. For example, the first hypothesis in Table 6, i.e., \"NNRsingle\" \(>\)\(k\)-NNRsingle\", is supported by repeated experiments because of its \(t\)-value \(>\) 0 and \(p\)-value \(<\alpha\), and it also implies that NNRsingle has a bigger prediction error; therefore, \(k\)-NNRsingle is a better prediction technique. On the other hand, if \(t\)-value \(<\) 0 and \(p\)-value \(<\alpha\), it means that the opposite hypothesis, i.e., \"NNRsingle\" \(<\)\(k\)-NNRsingle\", is supported, and it indicates that NNRsingle is better.  
Consequently, based on the performance differences that were found statistically significant at \(p\)-value = 0.05, the eight prediction techniques could be arranged in descending order of either MAE or RMSE, as \"MLRmulti \(>\) DTRmulti \(>\) NNRsingle\" \(>\)\(k\)-NNRmulti \(>\)\(k\)-NNRsingle \(=\) NNRmulti \(=\) RFRmulti \(>\) SVRmulti\". Additionally, given that the smallest values in performance metrics indicate the best prediction technique, it is statistically evident from Table 6 that SVRmulti was the best prediction technique for this study. Moreover, all the hypotheses related to SVRmulti proved the superiority of SVRmulti, which is highlighted in red font in Table 6. Therefore, SVRmulti was selected to train an age predictor with the labeled dataset to predict the age group distribution of anonymous commenters on unlabeled news articles.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Prediction** & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{3-6}  & **Technique** & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline \multirow{2}{*}{Single output} & NNRsingle & 0.0285 & 0.0003 & 0.0396 & 0.0004 \\ \cline{2-6}  & \(k\)-NNRsingle & 0.0267 & 0.0001 & 0.0379 & 0.0003 \\ \hline \multirow{6}{*}{Multiple output} & MLRmulti & 0.0360 & 0.0003 & 0.0805 & 0.0010 \\ \cline{2-6}  & NNRmulti & 0.0264 & 0.0003 & 0.0383 & 0.0010 \\ \cline{1-1} \cline{2-6}  & DTRmulti & 0.0357 & 0.0001 & 0.0511 & 0.0003 \\ \cline{1-1} \cline{2-6}  & SVRmulti & **0.0246** & 0.0001 & **0.0357** & 0.0003 \\ \cline{1-1} \cline{2-6}  & \(k\)-NNRmulti & 0.0270 & 0.0001 & 0.0381 & 0.0003 \\ \cline{1-1} \cline{2-6}  & RFRmulti & 0.0252 & 0.0001 & 0.0359 & 0.0003 \\ \hline \hline \end{tabular} Note: The best evaluation result for each performance metric is shown in bold highlight font.  
\end{table}
Table 5: Evaluation results for different prediction techniques.