## 4 Methodology and Experimental Results
### Results for Classification Algorithms
It appears that the best-performing setting is with the bag size percentage set to 1% while performing 10,000 iterations. So, we conclude with a well-known fact that supports our results: increasing iterations also increases performance, as does decreasing the bag size. The best run in these tests returned over 60% of correctly classified instances, while the worst, with bag size set to 100% and 10 iterations, returned a below-average 42.4%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{6}{c}{**Random Forest Results per Setting**} \\ \hline \multicolumn{6}{c}{All 17 Features} \\ \hline Classifier & CCI & ICI & Pre & Re & F1 \\ \hline -P 1 -I 10 & 55.8625 & 44.1375 & 0.559 & 0.559 & 0.558 \\ \hline -P 1 -I 1000 & 60.3 & 39.7 & 0.604 & 0.603 & 0.602 \\ \hline -P 1 -I 10,000 & 60.475 & 39.525 & 0.606 & 0.605 & 0.604 \\ \hline -P 50 -I 10 & 47.225 & 52.775 & 0.472 & 0.472 & 0.471 \\ \hline -P 50 -I 1000 & 46.3625 & 53.6375 & 0.464 & 0.464 & 0.463 \\ \hline -P 100 -I 100 & 41.2375 & 55.7625 & 0.442 & 0.442 & 0.442 \\ \hline -P 100 -I 1000 & 45.025 & 54.975 & 0.450 & 0.450 & 0.450 \\ \hline \multicolumn{6}{c}{5 Features} \\ \hline Parameters & CCI & ICI & Pre & Re & F1 \\ \hline -P 1 -I 10 & 54.375 & 45.625 & 0.545 & 0.544 & 0.542 \\ \hline -P 1 -I 1000 & 58.8875 & 41.1125 & 0.590 & 0.589 & 0.588 \\ \hline -P 1 -I 10,000 & 59.1625 & 40.8375 & 0.592 & 0.592 & 0.591 \\ \hline -P 50 -I 10 & 46.775 & 53.225 & 0.468 & 0.468 & 0.467 \\ \hline -P 50 -I 1000 & 45.3 & 54.7 & 0.453 & 0.453 & 0.453 \\ \hline -P 100 -I 100 & 42.1 & 57.6 & 0.421 & 0.424 & 0.424 \\ \hline -P 100 -I 1000 & 43.825 & 56.175 & 0.438 & 0.438 &Drawing definitive conclusions is challenging since, based on the above, the fact that increasing the bag size yields worse results (even by about 13% to 17% in some cases) might suggest an overfitting issue. On the other hand, we cannot say this definitively since when setting the bag size to 100% (which is a technique prone to overfitting), we unexpectedly obtain even lower correctly classified instances than when using 50% or 1%. Through the graphical visualization of the results (Figures 5 and 6), we observe that both lines representing tests using 5 and 17 features have a similar curve, despite the accuracy being lower in the case of the 5-feature set. We also observe a decrease in accuracy when increasing the number of iterations during the test runs with a bag size of 50%, but considering all of our previous tests, we can say that, in general, increasing the number of iterations increases the accuracy slightly, as does the bag size percentage, at least in our setup. We should also clarify that we could not run tests for 10,000 iterations with bag sizes of 50% and 100% due to hardware and tool limitations. The model building either took too long or WEKA ran out of memory and constantly crashed. Concluding this part of the research, we present the graph in Figure 7, which shows the best and worst percentages of correctly classified instances per feature group in our tests, regardless of the classifier used. Surprisingly, through our classifier tuning, we achieved almost identical performance across all sets and algorithms. There were certain exceptions like KNN with a single neighbor and Random Forest without parameterization. We also observed that when using all of our available features, we generated the best results, while the lowest accuracy was recorded with McAlpine EFLAW as a single feature. However, the difference was so small (\(\lesssim\)3%) that we cannot say that EFLAW underperformed. In addition, McAlpine EFLAW showed high tolerance across all of our tests, with the best and worst performance differing by only 1.97%, while the rest of the feature sets exhibited some fluctuations in performance depending on the algorithm used. The same can be said for the Flesch-Kincaid set with word count to some extent. These fluctuations can be attributed to the number of features used.