# Multi-Angle Fast Neural Tangent Kernel Classifier
## 5 Analysis of Experimental Results  
### Comparison of Multi-Model Results  
Based on the multi-kernel-NTK model established above, we substituted the collected UCI dataset for the analysis; the selection of parameters in training follows the settings in Section 4.1. Other details follow the settings in Section 4.2. Using 70% of the dataset as the training set, we divided each original dataset 20 times and ensured that each training set and test set contained all categories. We summarized some of the training data in Table 2 and performed a statistical analysis. The optimal results for different datasets have been blacked out. In these experiments, we used different parameters for each model. For the POLY-based model, C was selected from \(2^{-10}\) to \(2^{10}\), coef0 was selected from 0.02 to 2, and degree was set at 1, 2, 3; for the RBF-based model, C was selected from \(2^{-10}\) to \(2^{10}\), gamma was chosen from 0.1 to 50; for NTK based model, C was selected from \(2^{-8}\) to \(2^{7}\), dep was selected from 1 to 6. Experimental results show that the multi-kernel-NTK support vector classifier model outperforms the RBF POLY support vector machine classifier, random forest, AdaBoost, and RBF-Boolean and POLY-Boolean models in more than 90% of different domain datasets and in datasets that do not reach optimal performance, the multi-kernel-NTK version is also very close to the optimal performance. Moreover, the accuracy of the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **POLY** & **M-POLY** & **RBF** & **M-RBF** & **NTK** & **M-NTK** & **B-NTK** \\ \hline breast-cancer & 0.808 & 0.857 & 0.788 & 0.808 & 0.818 & 0.858 & 0.831 \\ credit-approval & 0.884 & 0.892 & 0.884 & 0.872 & 0.883 & 0.901 & 0.825 \\ echocardiogram & 0.892 & 0.881 & 0.862 & 0.896 & 0.854 & 0.898 & 0.942 \\ fertility & 0.9 & 0.9 & 0.9 & 0.91 & 0.9 & 0.92 & 0.875 \\ hepatitis & 0.805 & 0.799 & 0.844 & 0.822 & 0.818 & 0.882 & 0.856 \\ libras & 0.772 & 0.77 & 0.689 & 0.702 & 0.817 & 0.846 & 0.775 \\ Parkinsons & 0.918 & 0.873 & 0.938 & 0.883 & 0.938 & 0.968 & 0.877 \\ bridges-MATERIAL & 0.868 & 0.888 & 0.887 & 0.898 & 0.887 & 0.887 & 0.928 \\ bridges-REL-L & 0.784 & 0.845 & 0.824 & 0.875 & 0.765 & 0.875 & 0.755 \\ bridges-SPAN & 0.696 & 0.685 & 0.674 & 0.695 & 0.739 & 0.803 & 0.799 \\ bridges-T-OR-D & 0.902 & 0.943 & 0.902 & 0.943 & 0.887 & 0.907 & 0.926 \\ bridges-TYPE & 0.654 & 0.697 & 0.654 & 0.728 & 0.716 & 0.728 & 0.738 \\ german-credit & 0.76 & 0.772 & 0.75 & 0.798 & 0.786 & 0.779 & 0.770 \\ trains & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 & 0.8 & 0.6 \\ wine & 0.989 & 0.953 & 0.978 & 0.908 & 0.978 & 0.989 & 0.870 \\ zoo & 0.96 & 0.96 & 0.96 & 0.9 & 0.9 & 0.9 & 0.96 & 0.8 \\ [PERSON] test & & & \(H_{0}:e_{1}=e_{2}=e_{3}=e_{4}=e_{5}=e_{6}=e_{7}\) & & & & \\  & & & \(p=0.000\)(_reject_\(H_{0}\)) & & & & & \\ [PERSON] test & & & \(H_{0}:e_{1}=e_{2}=e_{3}=e_{4}=e_{5}=e_{6}=e_{7}\) & & & & \\  & & & \(p=0.000\)(_reject_\(H_{0}\)) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model results for selected datasets. For the NTK based model, C was selected from \(2^{-8}\) to \(2^{7}\), dep was selected from 1 to 6; for the Boolean kernel we used, C was selected from \(2^{-8}\) to \(2^{10}\), and d was selected from 0.003 to 32. The second column in Table 3 shows the proportion of Boolean-type features extracted from the original dataset to the total number of parts, and the third column shows the percentage of time resources saved by the multi-kernel-NTK model compared with the original NTK classifier. The third column shows the improved classification accuracy of multi-kernel-NTK compared with the NTK model. The fourth column shows the percentage of time resources saved by the Block-NTK model compared with the original NTK classifier. The last column shows the improved classification accuracy of Block-NTK compared with the NTK model.