%0 Journal Article
%T 机器学习驱动的二手车价格预测方法研究<br>Research on Machine Learning-Driven Used Car Price Prediction Method
%A 黄子敬
%J Computer Science and Application
%P 766-770
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.155149
%X 本文研究了利用机器学习方法预测二手车价格。作者使用了来自和鲸社区的二手车交易数据集&#65292;并构建了Adaboost、Bagging和LightGBM三种模型进行预测。通过MAPE和R<sup>2</sup>两个指标评估模型性能&#65292;结果显示Bagging模型表现最佳&#65292;MAPE为0.1180&#65292;R<sup>2</sup>为0.9027。<br>This paper studies the prediction of second-hand car prices using machine learning methods. The author utilized the used car transaction dataset from the Hejing community and constructed three models, namely Adaboost, Bagging, and LightGBM, for prediction. The performance of the model was evaluated through two indicators, MAPE and R<sup>2</sup>. The results showed that the Bagging model performed the best, with an MAPE of 0.1180 and a R<sup>2</sup> of 0.9027.
%K 机器学习&#65292
%K 二手车价格&#65292
%K Bagging<br>Machine Learning
%K Second-Hand Car Price
%K Bagging
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=116008