## 3 Results
### Calculation of Correlation Degree of Each Model  
In the first level of stacking models, a more diverse model is selected as the primary learner. This is because different algorithms observe data in different data spaces and data structures and build corresponding models according to their respective algorithm rules. It effectively combines the advantages of various model algorithms, reduces the risk of low-accuracy performance of a single model, and is suitable for high-dimensionality and small-sample data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Value** \\ \hline Linear Regression & & \\ \hline KNN & The number of neighboring points & 3 \\ \hline RandomForest & Number of decision trees & 100 \\ Maximum characteristic number & “sqrt” \\ Maximum depth of decision tree & 3 \\ \hline GradientBoost & Learning rate & 0.1 \\ Number of decision trees & 100 \\ Maximum characteristic number & “sqrt” \\ Maximum depth of decision tree & 3 \\ \hline ExtraTrees & Number of decision trees & 800 \\ Maximum distribution  
Therefore, based on ensuring the accuracy of prediction, the selection of algorithms with large differences can maximize the advantages of different algorithms so that each differentiated model can learn from each other. Diversity and difference make the ensemble learning results more robust and accurate and improve the prediction effect. Considering the complexity of models, the number of basic learning models should not be too large. Metrics such as [PERSON]'s correlation coefficient [62], [PERSON]'s correlation coefficient [63], and [PERSON]'s correlation coefficient [63] in the existing methods are widely used to measure the linear relationship between features or simple monotonous nonlinear data, but they struggle to well represent the existing nonlinear relationship; the computational complexity of the kernel density estimation (KDE) [64] algorithm and KNN metrics is too high. In this paper, the maximal information coefficient (MIC) [34] is selected to measure the linear or nonlinear correlation strength between the selected models, and the prediction errors of each model are used for calculation. Compared with the correlation coefficient, MIC can not only measure the linear and nonlinear relationship between variables in a large amount of data, but also can widely dig out the nonfunctional dependence relationship between the response variables. In addition, its computational complexity is low, and its robustness is strong. The larger the MIC value, the higher the importance of the feature to the response variable. The definition of mutual information is  
\[I(X;Y)=\sum_{x\in X}\sum_{y\in Y}p(x,y)\log\frac{p(x,y)}{p(x)p(y)}, \tag{26}\]  
where \(X=\{x_{i},i=1,2,\ldots,n\}\) and \(Y=\{x_{i},i=1,2,\ldots,n\}\) are two random variables, and \(n\) is the sample size. \(p(x)\) and \(p(y)\) are the marginal probability distribution densities of X and Y. Maximum mutual information is defined as  
\[mic(x,y)=\max_{|X||Y|<B}\frac{\max(I(X,Y))}{\log_{2}(\min(|X|,|Y|))}, \tag{27}\]  
where \(\max(I(X,Y))\) represents the maximum mutual information value; \(B\) is the upper limit of grid partitioning and is a growth function related to the number of data samples \(n\). \(B=n^{0.6}\) has the best effect, and this paper takes the optimal value. To optimize the performance of the stacking model in this paper, after completing the hyperparameter optimization of each primary learner to improve learning ability, it is  
Figure 9: Relative error of prediction of each single model: The error between the predicted value and the actual value of each model. necessary to further consider the correlation between the models and select the first-layer base learner with large differences. On the premise of ensuring the accuracy of prediction, it is necessary to add different kinds of prediction algorithms as much as possible.