## 6 Analysis of Experiment Results
### Algorithm Accuracy  
Tables 1-6 present the results of TWNFI, TR-SVM and Naive Bayes classifier evaluation testing of accuracy for a different amount of samples in a subset of data on the first cardiovascular disease dataset, for 100, 250, 500, 1000, 2500, 5000, 1000 samples and the whole set, respectively. For 100 samples, the TRSVM performs with the best accuracy (at 72%), followed by the Complement Naive Bayes classifier, followed by TWNFI, followed by the Gaussian Naive Bayes classifier. While the TRSVM is shown to be the most accurate, it also takes the most time to both fit and train its model. The Complement NB classifier is second in accuracy, and computes a lot faster, but loses to TRSVM by an entire 12%. TWNFI and Gaussian NB classifier both lose in this scenario, with the latter's accuracy going below 50%, and its benefits of a faster computation time being rendered irrelevant (Table 1). For 500 and 1000 samples, we observe a situation, with both the TRSVM and the TWNFI coming out with better results, with the former's computational times increasing further, and the latter's computational times decreasing bit by bit. The NB classifiers, however, in addition to computational time increase, perform considerably worse here, with their accuracy decreasing by 6-14% and 2-8%, respectively (Tables 2 and 3). For 2500 samples, we see that the TRSVM begins to perform worse, with it coming second to the TWNFI (itself at 76%) with 68%. Its training time also reaches approximately 9 h at this point, making further deployment impractical. On the other hand, the TWNFI both decreases its computational time and increases its accuracy. Both NB classifiers, meanwhile, despite remaining relatively fast, do not give satisfactory accuracy, with 54% and 40% for Gaussian NB and Complement NB classifiers, respectively (Table 4). \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **TR-SVM** & **TWNFI** & **NB Classifier (Gaussian)** & **NB Classifier (Complement)** \\ \hline Accuracy percentage & 75\% & 50\% & 42\% & 60\% \\ \hline Avg. fitting time (ms) & 106,353.96 & 27,047.08 & 0.6966 & 0.9453 \\ \hline Avg. prediction time (ms) & 53,244.02 & 1.4250 & 0.1470 & 0.1038 \\ \hline Mean square error (for 50 experiments) & 0.28 & 0.5 & 0.58 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of chosen machine learning algorithms for 100 samples. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **TR-SVM** & **TWNFI** & **NB Classifier (Gaussian)** & **NB Classifier (Complement)** \\ \hline Accuracy percentage & 70\% & 66\% & 54\% & 52\% \\ \hline Avg. fitting time (ms) & 4,629,342.37 & 20,456.25 & 1.2287 & 1.2396 \\ \hline Avg. prediction time (ms) & 425,968.14 & 0.5530 & 0.1445 & 0.07388 \\ \hline Mean square error (for 50 experiments) & 0.3 & 0.34 & 0.46 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of chosen machine learning algorithms for 500 samples. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **TR-SVM** & **TWNFI** & **NB Classifier (Gaussian)** & **NB Classifier (Complement)** \\ \hline Accuracy percentage & 70\% & 64\% & 46\% & 58\% \\ \hline Avg. fitting time (ms) & 12,611,525.95 & 5604.68 & 1.9520 & 2.070 \\ \hline Avg. prediction time (ms) & 543,569.71 & 0.4675 & 0.1426 & 0.0760 \\ \hline Mean square error (for 50 experiments) & 0.3 & 0.36 & 0.54 & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of chosen machine learning algorithms for 1000 samples. For 5000 samples, we observe a similar situation to the one with 2500 samples, with the TWNFI becoming even faster and more accurate on a larger subset, outputting 82% correct predictions. The quality of TRSVM, meanwhile, continues to decrease, dropping to around the same level as the NB classifiers, and further becoming slower compared to the other algorithms, with its training time now reaching almost 2 days. For 10,000 samples, the TRSVM run was terminated due to extremely long execution times (the program did not compute after several days) and Python _RuntimeError_.