## 3 Results and Discussion  
### Performance of the Seven Simple Classifiers  
In this section, we present the results for the performance of the seven simple classifiers mentioned above. Box plots, shown in Figure 2, were utilized to demonstrate the distribution of the accuracy that each one of the seven simple classifiers achieved on the test set. The red line inside each box represents the median (50 th percentile) of the data. Comparing the center lines of the box plots, we can see that the highest median is achieved by the Random Forest classifier, which suggests better average accuracy. The Decision Tree and Neural Network classifiers have also high average accuracy. The lowest accuracy is achieved by the KNN classifier. Furthermore, another key statistical measure is the Interquartile Range (IQR), which spans from the first quartile Q1 to the third quartile Q3. The height of the box indicates the spread of the middle 50% of the data. The larger the box, the more variability in the data. As shown in Figure 2, the Neural Network classifier has the highest variability and the Naive Bayes classifier has the lowest variability. Moreover, the lines extending from the box (whiskers) represent the range of the data. The highest range is demonstrated by the Neural Network classifier and the lowest range by the Naive Bayes classifier. The individual points represented as dots beyond the whisker are potential outliers, i.e., cases where the model achieved unusually high or low accuracy. Except for the Naive Bayes, Neural Network, and Random Forest classifiers, all the other models demonstrate signs of outliers. In all the cases, we observe outliers below the whisker, which means that these instances achieved lower accuracy than the majority of test cases. The results of the Naive Bayes classifier reinforce the opinion that is generally a robust model that performs well enough in many cases despite its simplification of variables. The overall sense for the performance is that models with higher medians and less variability perform better. In our case, these include the Decision Tree, Random Forest, and Neural Network models. Furthermore, we implemented, with the help of Python, multiple graphical representations for all classifiers and for several values of the test set size (30-90%) and the training set size (10-70%). The obtained results are presented in Figures 3 and 4, respectively. As can be seen, Decision Tree, Random Forest, and Neural Network score the highest accuracy. It is worth mentioning that for the Neural Network, the highest results were obtained when the test set size was 30% of the data, whereas Random Forest achieved the highest accuracy score when the test set size was 40% of the data and Decision Tree when the test set size was 30%. In all other cases, and especially when the test set size was 90%, the classifiers performed worst, except the Naive Bayes model, which, in this case, achieved the best accuracy. In addition to accuracy, additional metrics were utilized for further evaluation of the performance, i.e., Precision and Recall. Below, we give a short description of all the metrics for a better understanding of the results. True positive (TP): the number of patients positive for biopsy predicted as positive for biopsy. True negative (TN): the number of patients negative for biopsy predicted as negative for biopsy. False positive (FP): the number of patients negative for biopsy predicted as positive for biopsy. False negative (FN): the number of patients positive for biopsy predicted as negative for biopsy. Figure 2: Box plots for the performance of the seven simple classifiers on the test set. Accuracy: the proportion of test samples correctly predicted. \[\text{Accuracy}=\frac{(TP+TN)}{(TP+TN+FP+FN)}\]  
Precision: the proportion of positive-for-biopsy patient predictions that were correct. \[\text{Precision}=\frac{TP}{(TP+FP)}\]  
Figure 4: Multiple plots for the average accuracy for the seven classifiers vs. the size of the training set.