## 4 Methodology and Experimental Results
### Results for Classification Algorithms  
In this subsection, we present our results for the search for the optimal configuration. We ran tests for different feature sets using the following algorithms:* Naive Bayes;
* J48 Decision Tree (also known as C4.5);
* K-Nearest Neighbors;
* Sequential Minimal Optimization;
* Random Forest;
* Decision Table;
* Simple Logistic. We ran each of the above algorithms multiple times, modifying the parameters to attain optimal results, and then performed a comparative analysis to determine which was the most efficient. The first test used all the features previously introduced to distinguish between simple and complex sentences. Table 2 shows the results of our first test. The first column represents the algorithms/classifiers used and their parameters, where CCI stands for correctly classified instances. The top three test results are highlighted in three different shades of blue: the more vibrant shade represents the top result, and the lighter shades represent the next two best results. The worst result is highlighted in red. The best algorithm in this case appeared to be Random Forest, with a bag size equal to 1 and 10,000 iterations, achieving 60.5625% accuracy. We explain more about its parameterization later in this paper. It was followed by Naive Bayes with supervised discretization, achieving 59.9875%, and KNN (using the 3501 nearest neighbors, almost half the dataset's size) achieving the same result. In NB, each feature was conditionally independent of other features. We believe that the reason for the aforementioned accuracy value is the size of the dataset and the fact that in our feature set, the likelihood was evenly distributed, and our results throughout the tests may support this. J48 also performed well and managed to increase accuracy slightly. By setting the confidence interval for pruning extremely low to just 0.05, we obtained slightly better results (almost a 2% increase). By reducing the CI, we also reduced the depth of the J48 tree through pruning. As expected, adding the unpruned option (-U) made things worse, as the wider and deeper tree caused an overfitting effect. Overfitting happens when the model is unable to generalize and instead fits too closely to the training dataset. We keep in mind that outliers in our 8 K dataset could have also played a role. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**All Features**} \\ \hline
**Classifier** & **CCI** & **ICI** & **Pre** & **Re** & **F1** \\ \hline NB & 57.7 & 42.3 & 0.583 & 0.577 & 0.569 \\ \hline NB-K & 59.725 & 40.275 & 0.599 & 0.589 & 0.594 \\ \hline NB-D & 59.9875 & 40.0125 & 0.600 & 0.600 & 0.600 \\ \hline J48 & 58.3375 & 41.6625 & 0.585 & 0.583 & 0.581 \\ \hline J48-C 0.05 & 59.7625 & 40.2375 & 0.598 & 0.598 & 0.598 \\ \hline J48-U & 54.25 & 45.75 & 0.544 & 0.543 & 0.539 \\ \hline IBK & 41.7 & 58.3 & 0.116 & 0.117 & 0.118 \\ \hline IBk-K 13 & 56.0375 & 43.9625 & 0.560 & 0.560 & 0.560 \\ \hline IBk-K 71 & 59.3125 & 40.6875 & 0.597 & 0.593 & 0.589 \\ \hline IBk-K 551 & 58.9125 & 41.0875 & 0.590 & 0.589 & 0.588 \\ \hline IBk-K 3501 & 59.9875 & 40.0125 & 0.600 & 0.600 & 0.600 \\ \hline SMO & 58.5625 & 41.4375 & 0.586 & 0.586 & 0.585 \\ \hline Random Forest & 45.35 & 54.65 & 0.453 & 0.454 & 0.453 \\ \hline Random Forest-P 25 & 51.4 & 48.6 & 0.514 & 0.514 & 0.514 \\ \hline Random Forest-P 1 & 59.925 & 40.075 & 0.600 & 0.599 & 0.599 \\ \hline Random Forest-P 1 -110.000 & 60.5625 & 39.4375 & 0.607 & 0.606 & 0.605 \\ \hline Decision Table & 59.675 & 40.325 & 0.597 & 0.597 & 0.596 \\ \hline Simple Logistic & 59.5875 & 40.4125 & 0.596 & 0.596 & 0.596 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results from WEKA using 10-fold cross-validation and all our aforementioned features. The worst-performing algorithm was KNN with exactly one neighbor, underperforming significantly compared to the others. It was common across all our tests that KNN with fewer neighbors performed worse on this dataset.