摘要
Robotics&Machine Learning Daily News的一位新闻记者兼新闻编辑——根据基于预印摘要的新闻报道，我们的记者从OS F.IO获得了以下引用：“梯度提升是一种广泛使用的机器学习算法，用于表格的调整、分类和排名。尽管大多数梯度提升的开源实现，如XGBoost，LightGBM和其他人已经使用决策树作为梯度提升的唯一基估计。“本文首次提出了一种替代路径，不仅依赖于静态基估计器(通常是决策树)，而是根据前一层的残余误差并行训练M个模数列，然后选择具有最小验证误差的模型作为特定层的基本估计器。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from os f.io: “Gradient boosting is a widely used machine learning algorithm for tabular regre ssion, classification and ranking. Although, most of the open source implementat ions of gradient boosting such as XGBoost, LightGBM and others have used decisio n trees as the sole base estimator for gradient boosting. “This paper, for the first time, takes an alternative path of not just relying o n a static base estimator (usually decision tree), and rather trains a list of m odels in parallel on the residual errors of the previous layer and then selects the model with the least validation error as the base estimator for a particular layer.
NETL
NSTL