论文标题
基于机器学习的公司投资价值评估
Evaluation of company investment value based on machine learning
论文作者
论文摘要
在本文中，基于全面的公司信息建立了公司投资价值评估模型。在数据挖掘和提取一组436个特征参数之后，通过通过基于树的特征选择缩小维度来获得最佳特征子集，然后使用XGBoost和LightGBM模型进行5倍的交叉验证。结果表明，根平方误差（RMSE）分别达到3.098和3.059。为了进一步提高稳定性和概括能力，贝叶斯山脊回归已被用于训练基于XGBoost和LightGBM模型的堆叠模型。相应的RMSE最高为3.047。最后，分析了不同特征对LightGBM模型的重要性。
In this paper, company investment value evaluation models are established based on comprehensive company information. After data mining and extracting a set of 436 feature parameters, an optimal subset of features is obtained by dimension reduction through tree-based feature selection, followed by the 5-fold cross-validation using XGBoost and LightGBM models. The results show that the Root-Mean-Square Error (RMSE) reached 3.098 and 3.059, respectively. In order to further improve the stability and generalization capability, Bayesian Ridge Regression has been used to train a stacking model based on the XGBoost and LightGBM models. The corresponding RMSE is up to 3.047. Finally, the importance of different features to the LightGBM model is analysed.
