# Evaluation of Table Grape Flavor Based on Deep Neural Networks
## 4 Experiments and Performance Evaluation
### Evaluation Results  
#### 4.4.1 Comparison of Different Models  
To evaluate the performance of the proposed model, we conducted a controlled experiment to compare different models, using RMSE, R\({}^{2}\), AIC, accuracy, precision, recall, and F1-score as the evaluation indicators. The experimental results are provided in Table 7.  
We compared four different models, namely, Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), and BP Neural Network (BPNN). In general, our model performs better with respect to all evaluation metrics. Specifically, for the rating prediction, our model performs the best in terms of RMSE at 2.657, which indicates that our model accurately predicts the grape flavor with less error. Meanwhile, our model performs well in terms of R\({}^{2}\) at 0.987, implying that the variability in the target variables is well explained. AIC evaluates the explanatory performance and complexity of the model. Our model performs the best in terms of AIC, which indicates \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Parameter** \\ \hline CPU & Intel(R) Xeon Silver 4114 \\ Memory & 128 GB DDR4 \\ Operating System & Ubuntu18.04 STL \\ Graphics Card & GTX1060 Ti \\ Disk & 1 TB SSD \\ Program Language & Python 3.7 \\ Framework & PyTorch 1.6 \\ IDE & Pycharm 2020.3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental environment configuration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **RMSE** & **R\({}^{2}\)** & **AIC** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline SVM & - & - & - & 0.728 & 0.685 & 0.698 & 0.691 \\ Random & - & - & - & 0.795 & 0.751 & 0.776 & 0.763 \\ Forest & 6.458 & 0.701 & 800.119 & 0.845 & 0.820 & 0.826 & 0.823 \\ CNN & 5.748 & 0.855 & 550.927 & 0.852 & 0.832 & 0.866 & 0.835 \\
**Our Model** & **2.657** & **0.987** & **206.472** & **0.948** & **0.920** & **0.936** & **0.928** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of different models. that our model better explains the nature and characteristics of grapes and performs better in prediction. In terms of classification metrics, our model consistently performs better, achieving an accuracy of 0.948, which indicates that our model classifies grape flavors into different categories with a high degree of accuracy. Our model also achieves a better performance in terms of precision, recall, and F1-score, which indicates that our model has high comprehensiveness and reliability in classifying different categories of grape flavors. SVM performs poorly in the grape flavor multi-classification problem, achieving the lowest accuracy and F1-score. Note that SVM is a binary classifier, and it requires multiple binary classifiers for multi-category classification problems, which can be complex and computationally intensive. In addition, SVM is not suitable for handling nonlinear problems. Random Forest (RF) performs better in multi-classification problems than SVM. RF directly handles multi-category classification problems with better generalization ability and noise immunity. It uses multiple decision trees to reduce overfitting and can handle nonlinearity and missing data. However, it is generally less effective than neural networks for nonlinear problems. CNN and BPNN perform better in the grape flavor evaluation problem. Due to the powerful representation capability of neural networks, they can better learn and represent the complex relationships in the data. CNN is stronger in extracting local features using convolutional and pooling layers and weaker in handling global relationships. BPNN uses fully connected neural networks to fit the data and can adaptively adjust the weights and biases to better fit complex multi-category classification problems, making it a more effective solution. The model in this paper is based on DNN, with models such as embedding and attention added to improve its expression ability and achieve the best prediction results.