## 4 Methodology
### Decision Tree Classifier  
We tested the classifier using:  
* Data from the original dataset. * Data subjected to resampling through the utilization of SMOTE and its extensions, with the aim of equalizing the distribution among categories. Due to the close similarity between the original data and the synthetic data, coupled with the augmented representation of minority categories, it is anticipated that the performance of the data mining algorithms will exhibit an unusually high level of accuracy should the resampled dataset be utilized as the training set, while the original dataset is designated for the testing set. Consequently, this amalgamation of training and testing data would render it unfeasible to assess the efficiency of the algorithms because the test data should ideally differ significantly from the training data but now shares notable similarities. In order to effectively mitigate this issue, it is imperative that we adhere to the practice of segregating the original dataset into distinct training and testing sets. Subsequently, we should apply SMOTE and its associated extensions exclusively to the training set. We partitioned the dataset into two distinct subsets: a training set, encompassing 70% of the total data, and a testing set, comprising the remaining 30%. Following this partition, the training set consisted of 68 samples, while the testing set contained 28 samples. Subsequently, we applied SMOTE to the training set. This resampling procedure resulted in a noteworthy alteration of the training data's dimensions, increasing it to a total of 192 samples. The execution results of the decision tree classifier for a split of 70% (training set) and 30% (testing set) are shown in Table 2. This is a commonly used split ratio in the machine learning community [12] ensuring that a larger training set (70%) allows the model to learn from a substantial portion of the data, reducing bias. While the smaller testing set (30%) helps estimate the model's generalization performance with a diverse but independent subset, the \"Support\" value indicates the number of samples that exist in each category. We also present the decision tree as it was build in Figure 2. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
1 & 1.00 & 1.00 & 1.00 & 2 \\
2 & 0.71 & 1.00 & 0.83 & 5 \\
3 & 0.80 & 1.00 & 0.89 & 4 \\
4 & 1.00 & 0.50 & 0.67 & 2 \\
5 & 0.91 & 0.77 & 0.83 & 13 \\
6 & 1.00 & 1.00 & 1.00 & 2 \\ \hline Metric & & & & \\ \hline Accuracy & & & 0.86 & 28 \\ Macro Avg. & 0.90 & 0.88 & 0.87 & 28 \\ Weighted Avg. & 0.88 & 0.86 & 0.85 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decision tree classifier results. It is worth underscoring that the partitioning of the dataset into distinct training and testing sets has been accomplished through the application of stratified sampling. This method is predicated on the principle that the distribution of categories within the testing and training sets mirrors the original data's category distribution. This meticulous approach ensures an equitable representation of all categories, thus preventing any insufficiencies in either set. For elucidation, in our specific case, the composition of category samples, as shown in Table 2, reveals a hierarchy with respect to sample prevalence: category 5, followed by category 2, and subsequently category 3, among others. Consequently, this distribution of sample quantities in the stratified sets maintains parity with that observed in the original dataset, as elucidated in Table 1.  
Furthermore, it is imperative to note that the classifier's performance is profoundly contingent upon the splitting applied to the original dataset. This is because an expansion of the test set results in a diminished quantity of distinct samples within the training set, thereby impinging upon the classifier's performance, which is predicated on the diversity of the sample pool.