## 4 Experiments and Results
### Finger Classification Results  
After classifying the hand positions (in Section 4.4), \(k\)NN was found to perform poorly due to noise and inconsistency in labeling our movement labeling. Videos where guitarists performed poorly still contained some correct finger positions, which affected the hand position labeling. This problem was solved by labeling each finger position on a range of frames for each recorded video. Then, our classifier was trained using the same algorithms as in Table 5, which summarizes the accuracy scores for each labeled finger. Figures 11 and 12 show an example of the classification by the trained models for a frame with correct finger positions and another frame with incorrect finger positions and an incorrect technique (i.e., finger alignment in the A minor scale with only the index finger aligned correctly), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Algorithm**} \\ \hline
**Metric** & **SVM** & \(k\)**NN** & **Naive Bayes** &
\begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & **XG Boosting** \\ \hline Accuracy & 0.63 & 0.76 & 0.68 & 0.99 & 0.93 \\ Precision & 0.67 & 0.84 & 0.66 & 0.99 & 0.97 \\ Recall & 0.63 & 0.86 & 0.66 & 0.99 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hand position metric scores for various algorithms.  
SVM Tuning  
We ran a grid search on the SVM parameters, as its evaluation metrics were relatively low compared to the other tested algorithms. Sigmoid Kernel's highest score was 0.74 with  
Figure 11: Correct finger placement.  
Figure 12: Incorrect finger placement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**\(k\)NN**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**KGG Boosting**} \\  & & & & & \\ \hline \multicolumn{6}{c}{Accuracy} \\ \hline Index & 71\% & 97\% & 70\% & 98\% & 90\% \\ Middle & 81\% & 98\% & 68\% & 98\% & 90\% \\ Ring & 82\% & 98\% & 66\% & 97\% & 92\% \\ Pinky & 81\% & 98\% & 70\% & 97\% & 89\% \\ \hline \multicolumn{6}{c}{Precision} \\ \hline Index & 65\% & 97\% & 63\% & 96\% & 87\% \\ Middle & 83\% & 98\% & 64\% & 98\% & 94\% \\ Ring & 72\% & 98\% & 70\% & 98\% & 91\% \\ Pinky & 83\% & 97\% & 79\% & 97\% & 88\% \\ \hline \multicolumn{6}{c}{Recall} \\ \hline Index & 21\% & 97\% & 74\% & 95\% & 86\% \\ Middle & 28\% & 98\% & 81\% & 97\% & 91\% \\ Ring & 19\% & 98\% & 79\% & 97\% & 92\% \\ Pinky & 70\% & 97\% & 86\% & 96\% & 88\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-finger metric scores for various algorithms.  
the \(C\) parameter equal to 10. Our highest accuracy reached 0.981 using a \(c\) parameter of 1000 and a \(\gamma\) parameter equal to 1000. In this case, however, we suspected over-fitting as the score on the training set was higher than that on the test set. We settled for the most optimized selection consisting of \(c=10\) and \(\gamma=100\), reaching an accuracy of 0.96. While the random forest algorithm still scored higher, the SVM results after tuning were drastically improved. Figures 13 and 14 depict the index finger's model grid search process when using sigmoid and RBF kernels as well as just RBF kernels, respectively. Moreover, the highlighted values reveal that the scores of the sigmoid kernel are rapidly decreasing, whereas the rbf outperforms the sigmoid scores.