Research on machine learning strategy based on voting model

In this research, machine learning algorithms such as decision tree, random forest, and BP neural network are used to predict a certain dataset, and then a voting prediction model is built based on the above three machine learning algorithms. To verify the performance of this voting model, we introduced confusion matrix and F1 score to evaluate the effectiveness of machine learning. The experimental results show that the performance of the machine learning strategy based on the voting model outperforms that of a single machine learning algorithm and that adjusting the voting weights of a single algorithm can also affect the performance of the whole model. This result is well worth further study.