## 5 Conclusions and Future Work  
In this paper, we analyzed the performance of novel shallow feature sets on a specific Wikipedia dataset and presented results using a variety of optimally configured classifiers. Instead of the usual classification of whole texts or paragraphs, we performed classification at the sentence level, which is a step down in this hierarchy. This technically makes our classification task more challenging due to the lack of length and context in the data, but it can be more beneficial in certain aforementioned applications. Through supervised learning using WEKA, we ran multiple algorithms and presented the best-performing ones per case while attempting to analyze the reasons behind their performance. Additionally, we identified \"sweet spots\" in KNN parametrization concerning the number of neighbors, which appears to be around 1.8/4 of the total examples in our case. Furthermore, we researched in more depth the reasoning behind the efficiency of the Random Forest classifier applied to our dataset using our shallow feature sets. We came to the conclusion that increasing the number of iterations, as well as lowering the bag size percent, produces better results in our tests. As our final goal is to achieve higher accuracy and a better fit of features to the dataset, we highlighted useful observations relating to the nature of the algorithms and their features. We focused on testing features designed for (or at least suitable for) L2 readers, including infrequently used ones like the McAlpine EFLAW. EFLAW proved particularly stable and consistent across tests, and even when used as a single feature, it offered fair accuracy. This demonstrates an important point: with proper formulation and by only selecting features that accurately represent the nature of the dataset, even simple shallow  
Figure 9: Visualized clusters from the K-means algorithm. The Y-axis represents actual sentence complexity. Crosses and squares represent complex and simple examples, while colors indicate the different clusters formed. features can be reliable and have high tolerance in different tests run across various datasets and classifiers. In conclusion, we believe that using features specifically suited to the target audience of the text can lead to better or equally good accuracy. Moreover, we compared the classification and clustering approaches and concluded that they can be equally effective in this binary classification task. We observed that the problem hindering performance in classification was that many complex examples were assigned to the simple cluster rather than the opposite. Understanding more about the dataset and the way linguistics classifies sentences based on difficulty can improve this approach. Our peak accuracy with the classification algorithms was 60.56% using all of our employed features, while with the clustering algorithms, the highest accuracy reached was 59.6% using the feature set selected through the information gain algorithm, which showed a notable difference compared to the other sets. We observed that, regarding the classification approach, regardless of the feature set and algorithm used--with some minor exceptions for KNN and RF--the accuracy was surprisingly close, varying by less than 4-5%. In the future, we plan to expand our dataset and test additional features, including those from the linguistic feature family. Additionally, we aim to compare results from different tools and further optimize the classifiers. We also intend to include tests using neural networks and explore deep learning approaches, which we believe will lead to significantly better results. There seems to be a lack of formulas that consider surface-level characteristics created specifically for readability assessment of sentences. Further research into the linguistics behind this task should also be conducted in order to gain a better understanding of what EFL and L2 readers actually consider complex or simple.