Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM
Baoyang Cui, Zhonglin Ye, Haixing Zhao, Zhuome Renqing, Lei Meng, Yanlin Yang
Abstract
To better address the problem of the low prediction accuracy of used car prices under a large number of features and big data and improve the accuracy of existing deep learning models, an iterative framework combining XGBoost and LightGBM is proposed in this paper. First, the relevant data processing is carried out for the initial recognition features. Then, by training the deep residual network, the predicted results are fused with the original features as new features. Finally, the new feature group is input into the iteration framework for training, the iteration is stopped, and the results are output when the performance reaches the highest value. These experimental results show that the combination of the deep residual network and iterative framework has a better prediction accuracy than the random forest and deep residual network. At the same time, by combining the existing mainstream methods with the iterative framework, it is verified that the iterative framework proposed in this paper can be applied to other models and greatly improve the prediction performance of other models.