## 3 Results and Analysis
The remaining F1-Scores are close to this range, with the exception of GSE66053-GPL18573, which has a median of 0.72. Figure 2: Recall scores (y-axis) for each dataset (x-axis) used with the glm package. The bullets and dashed lines represents extreme values distribution. ### Area under the Curve

The AUC median values shown in Figure 4 provide insights into glm's prediction performance. It shows that the highest median is 0.76 in the SRP073808 dataset, and the lowest median is 0.62 in the GSE60749-GPL13112 dataset. Out of 22, 19 of the median scores fall within the range of 0.72 to 0.76, which indicates consistent performance. These AUC scores also demonstrate a similar interquartile range as well as, roughly, the same size of interquartile range. Only one AUC score deviates notably from this range: that of the GSE60749-GPL13112 dataset. Figure 4: AUC scores (y-axis) for each dataset (x-axis) used with the glm package. The bullets and dashed lines represents extreme values distribution. Figure 3: \(F1\)-_Scores_ (y-axis) for each dataset (x-axis) used with the package. The bullets and dashed lines represents extreme values distribution. ### Runtime

The Runtime analysis (in seconds) depicted in Figure 5 shows that the highest median value is 29.58 and the lowest is 0.53. The majority of the median scores (18 datasets) fall within the range of 0.53 to 5.35. However, there are four datasets (GSE57872, GSE66053-GPL18573, GSE71585-GPL17021, and GSE48968-GPL17021-125 bp) that demonstrate significantly higher runtime values of 11.72, 16.13, 19.87, and 29.58, respectively. It is unclear why these datasets took a longer time, as there are no correlations found between the size of these datasets and Runtime. ## 4 Discussion and Conclusions

Based on our analysis of the results presented in the previous section, we can deduce the following regarding each evaluation category. Regarding glm's Precision, there is a noticeable fluctuation in the results. That can be seen with the median value of fitting the GSE71585-GPL17021 dataset. Nevertheless, it has the smallest interquartile range, which indicates that it has the most accurate testing because its scores mostly do not fluctuate. Other interquartile ranges show some similarities in size, but their medians vary. Thus, when it comes to Precision, glm shows minimal performance. When considering Recall results, glm shows more stable performance. The majority of median scores are concentrated within a narrow range of 0.09, which highlights the excellent performance of glm. The high score of GSE71585-GPL17021's Recall is expected, as it aligns with its high Precision score mentioned earlier. While the large size of GSE71585-GPL17021 could potentially contribute to this high score, it is important to note that GSE57872, which is larger than GSE71585-GPL17021, does not exhibit the same pattern. Therefore, the high Recall score of GSE71585-GPL17021 cannot be solely attributed to its size. Figure 5: Runtime scores (y-axis) for each dataset (x-axis) used with the glm package. Dashed lines represents extreme values distribution. F1-Scores, on the other hand, show a fluctuating performance. This is largely connected to the glm's Precision because F1-Scores are derived from it. This is especially obvious when looking at both the Precision and F1-Score figures. Additionally, median values of Precision and _F1-Scores_, in Table 7, are nearly similar. Therefore, glm also has minimal performance when it comes to F1-Score. In the AUC category, glm shows remarkable performance. The AUC medians across the vast majority of tests are almost the same, and their interquartile ranges have the same length. This indicates that glm has a stable performance regardless of the size of the input data. The analysis of Runtime (in seconds) results indicates that the glm package demonstrates a consistent performance. The fluctuation of runtime occurs within a narrow time range of approximately 0.25 s. However, four datasets deviate significantly from this time range.