## 4 Methodology and Experimental Results
### Results for Classification Algorithms
Naive Bayes generally performed well across tests when these specific parameters were applied. Supervised discretization helped to create \"bags\" containing a range of values rather than multiple discrete ones. Generally, kernel density estimation and supervised discretization were expected to work better due to the distribution of our dataset, which benefited from such modification. Simple Naive Bayes assumed that the numerical data followed a normal distribution. In this case, supervised discretization achieved around 0.06% higher accuracy than kernel density estimation. Our tests suggest that either technique would work well. Figure 3: Visualization from the WEKA environment, showing the values of the features of the dataset examples per classification group. Red represents complex examples and blue represents simple ones. Once again, we observed that all algorithms performed similarly when tweaking some simple parameters, with the only ones that significantly underperformed being Random Forest with no parameterization and with a bag size set to 25%, as well as KNN with a single neighbor. In addition to the previously selected feature set, which consisted of five handpicked features through our custom selection process, we decided to create another feature set consisting of the top five features selected using the information gain (IG) algorithm through WEKA. IG allowed us to determine the importance or prominence of the features in our dataset based on their classification results. By evaluating our features through IG, we estimated their entropy, which is the amount of information (or surprise) inherent to the output variable's possible outcomes. Lower-probability (surprising) events have more information, while higher-probability ones (less surprising) have less. Information gain measures the reduction in entropy. Based on this evaluation, we selected the top five features with the highest information gain. A notable observation from the results of IG, as seen in Table 4, is that three out of the five features with the highest information gain were those that included less common variables in their formulas, rather than simply the number of words or characters. Spache readability includes unfamiliar words, Flesch-Kincaid considers syllables, and Gunning Fog takes into account complex words. This observation may be useful when creating features and formulas for similar datasets. We ran the same tests on the top five features set based on the IG selection, obtaining the results shown in Table 5. Again, Naive Bayes with supervised discretization achieved the highest accuracy of 59.95%, but the difference from other algorithms was negligible. All the results were once again very consistent, regardless of the algorithm and feature set used, reaching nearly 60%. Naive Bayes with kernel density estimation and KNN with 3501 neighbors followed, with under 0.3% worse performance. The unpruned version of J48 was again less effective than others, while KNN with a single neighbor was once again the worst. The results seemed to follow the same pattern as the previous tests.