论文标题
从您的回归模型中获取更多：免费午餐？
Getting more from your regression model: A free lunch?
论文作者
论文摘要
我们考虑了一种简单的方法，用于近似于仅使用来自标准回归模型的输出的实现响应变量的条件分布的详细信息，即其协变量的给定值。我们通过在分数回归的背景下评估其性能来验证这种方法；当应用于线性，梯度增强的树集合和随机森林模型时。我们发现，它与估计分位数回归函数的标准方法相比，尤其是对于常用的尾巴概率，并且在大量基准数据集中与分位数回归森林模型具有很高的竞争力。
We consider a simple approach for approximating detailed information about the conditional distribution of a real-valued response variable, given values for its covariates, using only the outputs from a standard regression model. We validate this approach by assessing its performance in the context of quantile regression; when applied to the outputs of linear, gradient boosted tree ensemble and random forest models. We find that it compares favourably to the standard approach for estimating quantile regression functions, especially for commonly selected tail probabilities, and is highly competitive with the quantile regression forest model, across a large collection of benchmark data sets.
