## 3 Results
### Model Training Behaviour Analysis  
In this section, we present the results on the learning behaviour of the models during training. We show the observed F1 score distributions from different training and validation data sizes both before and after data augmentation. For the first five models, we present learning behaviour curves based on the mean F1 scores with standard deviations. For the ANN model, we present learning curves from validation loss metrics at different epochs. Visualizations of the learning curves are presented in Appendix B. As seen in Figure A1, the learning curves of the LR on the original data exhibit minimal deviations throughout the different data sizes. The curves show decreasing deviations from average F1 scores and convergence over larger data sizes. On the augmented dataset, LR training and validation F1 curves converge at a 100% data size. However, there are observable deviation measurements of validation F1 scores for data sizes below 60%. Generally, LR learning curves show more noticeable performance improvement on the augmented dataset compared to the original dataset. For the SVM model, however, the learning curves (Figure A2) show different patterns of behaviour between the original and augmented dataset. The training F1 scores are higher for smaller data sizes between 20% and 40%, after which the F1 scores continuously decrease over larger datasets. The persistent decrease in performance is coupled with increased deviations from the mean. Model learning improvement for the SVM model on the augmented data is mainly seen in reduced deviations from the mean F1 score as well as narrowed gaps between training and validation accuracy. Interestingly, the learning curves from the augmented dataset exhibit a study decline in F1 scores as the proportion of training data increases from 40% to approximately 76%, after which the F1 scores begin to increase. Curves for the ensemble models show more marginal deviations and better learning behaviour. The curves for the RF model (Figure A3) on the original data report approximately 0.98 when trained on smaller data subsets. As the training data size increases, the F1 score gradually declines possibly due to the increased variability and noise introduced with  
Figure 8: Boxplots for 5-fold cross-validation on augmented dataset. more data. Nonetheless, training scores remain above 0.88. On the other hand, the metric associated with validation data increases with more training data, with F1 score improving from \(\sim\)0.76 to \(\sim\)0.84. The initial performance gap between training and validation scores is largest at small training sizes, indicating possible overfitting over smaller data sizes. Marginal deviations are visible on the validation F1 curve. With regards to augmented data, learning curves of the RF model indicate relatively high (approximately 0.93-0.95) training F1 measurements for data sizes below 20%. There are similar declines up to a \(\sim\)60-70% training size but then slightly increases towards the full data size. Validation curves, on the other hand, show a consistent upward trend across all training data sizes. F1 scores increases consistently from about 0.78 to approximately 0.89-0.91 at the full data size. Also, deviations from the average F1 score are smaller compared to those observed using the original data. As with the RF ensemble classifier, learning curves for the GBM model (Figure A4) report high F1 scores for smaller training subsets and continuous decline with larger subsets. Metric scores on validation data exhibit a gradual increase with observable deviations from the mean. When trained with the augmented data subsets, the decreasing trend of training learning curves over larger subsets is observed up to around 70% of training data, after which the metric scores tend to marginally rise toward the full dataset size. The validation learning curves for augmented data show steady growth with increasing data sizes.