论文标题
XGBOOST中的特征互动
Feature Interactions in XGBoost
论文作者
论文摘要
在本文中，我们研究了如何使用XGBoost的实现来确定如何将特征交互作用用作梯度增强树模型的约束。我们的结果表明，对这些约束的准确识别可以有助于显着提高基线XGBoost模型的性能。此外，模型结构的改进也可以提高更好的解释性。
In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results show that accurate identification of these constraints can help improve the performance of baseline XGBoost model significantly. Further, the improvement in the model structure can also lead to better interpretability.
