## 4 Discussion  
This article combines the characteristics of high-dimensional, small-sample regression prediction. UCI's real dataset is used to verify the feasibility and effectiveness of the algorithm. The box plot method and the MICE algorithm are used to detect and replace the outliers in the features in the database, respectively. In addition, OLS is used to further detect abnormal sample values with the help of studenti  
Figure 12: Comparison of prediction performance of stacking ensemble learning model, blending ensemble learning model, linear regression model, and SVR model. [PERSON]'s distance, and DFFITS distance to avoid the impact of strong influential points on the prediction accuracy. To further increase the accuracy of regression prediction, using the function of CatBoost feature combination, adding a strong combination feature as a new feature, and verifying that the new feature set is compared with the original feature, CatBoost shows an average \(R^{2}\) increase of 3.333% on the verification set and the test set. Box-Cox transformation was performed on the features with skewness greater than 0.5 to make them conform to the approximate normal distribution and standardize the data. Then, by dividing the database into a training set (70%), validation set (10%), and test set (20%), \(R^{2}\), RMSE, and MAE were used as model evaluation indicators. Through the data preprocessing, strong combination feature construction, and grid search methods in this paper, a single model has achieved good prediction results. Aiming at the problem that the single-model prediction accuracy is still insufficient in the actual production process, a regression prediction algorithm based on stacking model fusion is proposed. Different regression models were combined through the stacking method, and eight-fold cross-validation was used to prevent overfitting. Performing model correlation analysis before model establishment can effectively measure the differences between models. Maximizing model differences can effectively combine the advantages of single-model prediction algorithms. When one observes the data space and structure from different angles, different algorithms complement each other to obtain the best prediction model. Table 11 longitudinally compares the \(R^{2}\), RMSE, and MAE of the minimum correlation stacking ensemble learning model, the blending ensemble learning model, and the single model optimized by hyperparameters. Experiments show that the minimal correlation stacking model can achieve the best prediction effect, and \(R^{2}\), RMSE, and MAE are 0.9702, 0.0389, and 0.0304, respectively. The basic model of the stacking algorithm is used to synthesize the prediction results of the multidimensional prediction model, which overcomes the shortcomings of single model generalization and poor applicability. In a single model, the machine learning algorithms linear regression, GradientBoost, ExtraTrees, SVR, XGboost, CatBoost, RandomForest and KNN obtained \(R^{2}\) values of 0.8883, 0.8601, 0.8616, 0.8883,_0.8091, 0.8083, 0.7996, and 0.6593, respectively. The RMSE values were 0.0799, 0.0890, 0.08860,0.0788, 0.1043, 0.1025, 0.1068, and 0.1392, respectively. The MAE values were 0.0619, 0.0695, 0.0730, 0.0639, 0.0755, 0.0819, 0.0855, and 0.1162, respectively. The minimal correlation stacking model increased the \(R^{2}\) value by 8.19%, 11.01%, 10.86%, 8.19%, 16.11%, 16.19%, 17.06%, and 31.09%, and decreased the RMSE value by 4.1%, 5.01%, 4.97%, 3.99%, 6.54%, 6.36%, 6.79%, and 10.03%, respectively. The MAE values decreased by 3.15%, 3.91%, 4.26%, 3.35%, 4.51%, 5.15%, 5.51%, and 8.58%, respectively, showing satisfactory predictive performance. Compared with the blending ensemble learning method of optimal model combination, the proposed stacking model also achieved better results. Our study demonstrates that the stacking model has excellent accuracy and high application value in high-dimensional, small-sample regression prediction and shows unique advantages.