论文标题
多目标Xgboostlss回归
Multi-Target XGBoostLSS Regression
论文作者
论文摘要
梯度提升机的当前实现主要是为单目标回归任务而设计的，并且在多元设置中使用时通常在响应之间具有独立性。因此，如果目标之间存在不可忽略的依赖关系，那么这些模型并不适合。为了克服这一限制，我们提出了Xgboostlss的扩展，该扩展在概率回归设置中对多个目标及其依赖关系进行了建模。经验结果表明，我们的方法在运行时的表现优于现有的GBM，并且在准确性方面进行了很好的比较。
Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if non-negligible dependencies exist between targets. To overcome this limitation, we present an extension of XGBoostLSS that models multiple targets and their dependencies in a probabilistic regression setting. Empirical results show that our approach outperforms existing GBMs with respect to runtime and compares well in terms of accuracy.
