## 5 Experiment and Result Analysis
### Feature Data Selection
#### 5.1.3 Validation of Feature Validity  
Upon obtaining the mixed feature set, experiments were designed to validate its effectiveness. Using the Root Mean Square Error (RMSE), Mean Relative Error (MEANRE), Maximum Relative Error (MAXRE), and Coefficient of Determination (R2) as evaluation indices, experiments were designed to compare different numbers of residual attention units. The calculation formula for each evaluation index is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{\left(y_{i}-\tilde{y}_{i}\right)^{2}}} \tag{23}\]  
\[M\text{ean}RE=\frac{1}{n}\sum_{i=1}^{n}{\left(\frac{|y_{i}-\tilde{y}_{i}|}{y_ {i}}\times 100\%\right)} \tag{24}\]  
\[M\text{ax}RE=max{\left(\frac{|y_{i}-\tilde{y}_{i}|}{y_{i}}\times 100\%\right)} \tag{25}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{\left(y_{i}-\tilde{y}_{i}\right)^{2}}}{\sum_{i=1 }^{n}{\left(y_{i}-y_{mean}\right)^{2}}} \tag{26}\]  
In the above formula, n represents the total number of samples, \(y_{i}\) is the actual value of the sample, \(\tilde{y}_{i}\) is the predicted value of the model, and \(y_{mean}\) represents the average of all predicted values. We hope that RMSE, MEANRE, and MAXRE are small, and that \(R^{2}\) is close to 1, as these values reflect better accuracy and predictive ability on the part of the model. The BP neural network, Support Vector Machine (SVM), Gradient Boosting Regression Tree (GBDT), and Extreme Learning Machine (ELM) were established as the component content prediction models. The HSV feature set, the combination of HSV and YUV feature set, and the mixed feature set selected in this paper are divided into a training set and a prediction set. The parameters of these models were set and adjusted to their best for comparison and verification. The experimental results are shown in Table 1 and Figure 7.  
As per Table 1, while the MeanRE and RMSE of the BP and GBDT models under mixed features are superior, MaxRE is inferior to the HSV feature set. It is speculated that this may be due to the fact that for simpler models the mixed feature set may contain too many features, making the model susceptible to the influence of low importance features. This can affect the prediction performance of the model, resulting in poorer MaxRE results. Comparing the four prediction models, in most cases the combined performance using the mixed features is better than that using either HSV alone or HSV+YUV features. Figure 6: Feature importance score plot. As depicted in Figure 7, the overall performance of mixed features surpasses that of single color features under the MeanRE and RMSE indicators. The coefficient of determination (R2 index) for each model is higher with mixed features than with other features. Upon analyzing the combined features of HSV and HSY+YUV, it is evident that the average performance of the combined features of HSY+YUV is superior to that of using only HSV characteristics. The speculated reason for this is that although the HSV space can reflect \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MODEL** & **FEATURE** & **MeanRE** & **MaxRE** & **RMSE** & \(R^{2}\) \\ \hline \multirow{4}{*}{BP} & HSV & 2.275 & 2.275 & 1.583 & 0.997 \\  & HSV+YUV & 1.761 & 4.831 & 1.570 & 0.997 \\  & mixed & 1.366 & 3.859 & 1.476 & 0.997 \\  & features & & & & \\ \hline \multirow{4}{*}{SVM} & HSV & 2.274 & 9.358 & 1.520 & 0.997 \\  & HSV+YUV & 2.273 & 9.893 & 1.534 & 0.997 \\  & mixed & 1.324 & 4.254 & 0.887 & 0.999 \\  & features & & & & \\ \hline \multirow{4}{*}{GBDT} & HSV & 3.608 & 15.90 & 3.701 & 0.983 \\  & HSV+YUV & 3.507 & 14.21 & 2.639 & 0.991 \\  & mixed & 2.924 & 17.32 & 2.347 & 0.993 \\  & features & & & & \\ \hline \multirow{4}{*}{ELM} & HSV & 2.010 & 8.252 & 1.531 & 0.997 \\  & HSV+YUV & 1.863 & 8.409 & 1.364 & 0.997 \\  & mixed & 1.942 & 9.905 & 1.182 & 0.998 \\ \cline{1-1}  & features & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different feature sets. Figure 7: Comparison diagram of different feature sets. (**a**) MeanRE; (**b**) MaxRE; (**c**) RMSE; (**d**) R2.