# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 3 Results and Discussion
### Performance of the Model for Different Classifiers
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 91.4 & 0.91 & 0.82 & 91.1 & 90.5 & 91.5 \\ \hline
**RF** & **93.8** & **0.93** & **0.80** & **96.6** & **93.2** & **93.5** \\ \hline AdaBoost & 91.7 & 0.92 & 0.89 & 90.8 & 89.5 & 91.2 \\ \hline NB & 78.8 & 0.78 & 0.57 & 79.7 & 80.2 & 78.4 \\ \hline LR & 90.5 & 0.90 & 0.81 & 90.4 & 90.1 & 90.9 \\ \hline SGD & 86.1 & 0.85 & 0.71 & 86.7 & 88.3 & 89.3 \\ \hline Bernoulli NB & 87.6 & 0.87 & 0.75 & 90.1 & 88.3 & 87.4 \\ \hline DT & 90.0 & 0.90 & 0.80 & 89.1 & 89.5 & 91.7 \\ \hline RT & 91.7 & 0.91 & 0.90 & 92.5 & 92.3 & 91.7 \\ \hline \end{tabular}
\end{table} 
Table 3: The results of comparing machine learning algorithms based on various performance metrics using an independent test set for the Antifp_DS1 dataset. The ROC curves of all models on the independent test datasets--Antifp_DS1, Antiffp_DS2, and Antiffp_DS3--are displayed in Figures 2-4, respectively. As shown in these figures, RF demonstrates better results compared to other classifiers. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 91.4 & 0.91 & 0.82 & 90.9 & 90.5 & 92.3 \\ \hline RF & 93.7 & 0.92 & 0.86 & 94.3 & 93.2 & 96.8 \\ \hline AdaBoost & 88.3 & 0.87 & 0.85 & 85.2 & 89.5 & 87.2 \\ \hline NB & 77.2 & 0.74 & 0.55 & 84.2 & 80.2 & 80.6 \\ \hline LR & 91.7 & 0.91 & 0.83 & 90.8 & 90.1 & 92.9 \\ \hline SGD & 88.1 & 0.88 & 0.88 & 86.8 & 88.3 & 91.4 \\ \hline Bernoulli NB & 86.1 & 0.86 & 0.72 & 86.4 & 88.3 & 92.1 \\ \hline DT & 87.3 & 0.87 & 0.74 & 88.7 & 89.5 & 89.2 \\ \hline RT & 91.2 & 0.92 & 0.90 & 92.6 & 92.3 & 91.3 \\ \hline \end{tabular}
\end{table} 
Table 6: Results of the comparison of machine learning algorithms based on various performance metrics using 10-fold cross-validation for the Antiffp_DS3 dataset. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 90.8 & 0.91 & 0.81 & 89.9 & 90.5 & 92.3 \\ \hline
**RF** & **94.1** & **0.93** & **0.87** & **95.1** & **93.2** & **96.8** \\ \hline AdaBoost & 83.4 & 0.82 & 0.81 & 82.1 & 89.5 & 82.0 \\ \hline NB & 78.0 & 0.75 & 0.57 & 85.5 & 80.2 & 80.5 \\ \hline LR & 90.1 & 0.90 & 0.80 & 89.3 & 90.1 & 92.9 \\ \hline SGD & 88.3 & 0.88 & 0.76 & 89.6 & 88.3 & 91.4 \\ \hline Bernoulli NB & 86.9 & 0.86 & 0.73 & 89.6 & 88.3 & 92.1 \\ \hline DT & 87.6 & 0.87 & 0.75 & 90.1 & 89.5 & 89.2 \\ \hline RT & 92.2 & 0.90 & 0.92 & 91.8 & 92.3 & 91.2 \\ \hline \end{tabular}
\end{table} 
Table 7: The results of comparing machine learning algorithms based on various performance metrics using an independent test set for the Antiffp_DS3 dataset. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 93.1\% & 0.93 & 0.86 & 92.3 & 93.5 & 91.4 \\ \hline
**RF** & **96.2** & **0.96** & **0.92** & **96.8** & **96.4** & **93.8** \\ \hline AdaBoost & 92.3 & 0.91 & 0.90 & 91.4 & 92.6 & 91.1 \\ \hline NB & 85.6 & 0.89 & 0.86 & 80.5 & 88.9 & 78.8 \\ \hline LR & 93.6 & 0.94 & 0.87 & 92.9 & 92.5 & 90.5 \\ \hline SGD & 91.4 & 0.91 & 0.83 & 91.4 & 90.9 & 86.1 \\ \hline Bernoulli NB & 90.4 & 0.90 & 0.80 & 92.1 & 91.1 & 87.6 \\ \hline DT & 90.4 & 0.90 & 0.80 & 89.2 & 91.6 & 90.0 \\ \hline RT & 91.1 & 0.91 & 0.92 & 91.2 & 92.5 & 91.7 \\ \hline \end{tabular}
\end{table} 
Table 5: The results of comparing machine learning algorithms based on various performance metrics using an independent test set for the Antiffp_DS2 dataset.