Comparison of XGBoost and the Neural Network model on the class-balanced datasets
Jincenzi Wu, Yuanyuan Li, Yizhou Ma
Abstract
The Extreme Gradient Boosting method and Deep learning methods are classical machine learning methods widely used in many fields. However, the advantages and disadvantages of these two methods have been a long-debated topic. This paper evaluates the classification performance of the XGBoost and Multiple-Layer Perceptron Neural Network on the structured data. We compare the classification performance of both methods, using the large scale, public datasets, and show the overall trend with the different percentages of datasets used. The experiment validated the higher accuracy in all datasets obtained through the XGBoost method. We concluded that the XGBoost method overcomes the complex data distribution with the feature space to better classify performance on the structured data.