# Impact of Dataset Size on Classification Performance:
## 3 Results
### Large Datasets  
Figures 2 and 3 show the performance of the six classification models with respect to accuracy, precision, recall, f-score, specificity, and AUC when trained on the large datasets, namely diabetes and skin segmentation, respectively, across decreasing sizes of the training set. The \(x\)-axis in the figures shows the size of the dataset, namely large dataset (LD), small dataset of size 980 (SD980), small dataset of size 490 (SD490), and small dataset of size 98 (SD98). LD indicates that the full size of the large dataset, as shown in Table 3, is used for training for both diabetes and skin segmentation datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline
**Datasets** & **AB** & **RF** & **NN** & **DT** & **NB** & **SVM** & **Avg.** & **Std. Dev.** \\ \hline
**DS1** & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 99.00\% & 99.00\% & **99.00\%** & \(1.110\times 10^{-16}\) \\ \hline  
vg.** & 79.28\% & 82.39\% & 81.72\% & 82.78\% & 79.78\% & 80.61\% & \\ \hline
**Std. Dev.** & 0.153 & 0.123 & 0.118 & 0.117 & 0.131 & 0.115 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of classifiers trained on small datasets.  
Figure 2: Performance of classifiers with respect to (**a**) accuracy, (**b**) precision, (**c**) recall, (**d**) f-score, (**e**) specificity, (**f**) AUC when trained on diabetes dataset and its small subsets.  
Figure 3: Performance of classifiers with respect to (**a**) accuracy, (**b**) precision, (**c**) recall, (**d**) f-score, (**e**) specificity, (**f**) AUC when trained on skin segmentation dataset and its small subsets.  
In all figures, each line chart has three segments reflecting the result in three reduction scenarios of datasets size. The first segment ranges from LD to SD980 and shows the result in the first size reduction scenario, which we refer to as the LD-SD980 scenario. This line segment presents a key result in the chart as it depicts the change in performance of a classifier trained on a large dataset (LD) when trained on a small dataset of size 980 (SD980). The second segment in the line charts stretches from SD980 to SD490. It illustrates the change in performance of a classifier in the second size reduction scenario SD980-SD490, where the size of the dataset reduces from 980 (SD980) to an even smaller dataset of size 490 (SD490). In a similar manner, the third segment in the line charts extends from SD490 to SD98. It shows the change in performance of a classifier when the size of the dataset reduces from 490 (SD490) to a smaller dataset of size 98 (SD98), which we refer to as the third scenario SD490-SD98.  
Several observations can be made from these figures. First, most classifiers exhibit relatively similar trend of performance over decreasing training set size with respect to all six performance metrics. This can be seen by comparing the performance of one classifier across performance metrics. Second, there is a clear general trend of decreasing performance with respect to all metrics for almost all classifiers in all size reduction scenarios on both datasets, although the classifiers showed varying reactions to the different size reduction scenarios. The most striking observation is that the performance of the AB model increases as the diabetes dataset size decreases. Third, the best performing classifiers may vary across datasets. For instance, in the diabetes dataset (Figure 2), the best performing classifiers are SVM and NN, while, in the skin segmentation dataset (Figure 3), RF, DT, and NN perform the best. However, in both datasets, AB is the least performing classifier with respect to most performance metrics.