Application of Ensemble Learning Algorithm in Improving Prediction Accuracy and Model Stability

Ensemble learning algorithm has unique advantages in improving prediction accuracy, improving model stability, and alleviating data sample imbalance. As a common method of integrated learning, different algorithms are used to create models that show good stability and can fully utilize global data in second-layer fusion. This paper gives full play to the advantages of four single models such as “Bagging, XGBoost, SVM, Logistic Regression”, and the prediction results of three models such as “Bagging, XGBoost, SVM” are integrated as new eigenvalues to provide input for the second layer learning. The second layer architecture uses Logistic Regression and is retrained with new eigenvalues as inputs. The results of the Logistic Regression model and the primary learner are combined into a meta-learner to obtain the final prediction result. The model constructed in this paper reduces the calculation time and can obtain higher accuracy, which improves the accuracy of the prediction algorithm.