We present Natural Gradient Boosting (NGBoost), an algorithm which brings probabilistic prediction capability to gradient boosting in a generic way. Predictive uncertainty estimation is crucial in many applications such as healthcare and weather forecasting. Probabilistic prediction, which is the approach where the model outputs a full probability distribution over the entire outcome space, is a natural way to quantify those uncertainties. Gradient Boosting Machines have been widely successful in prediction tasks on structured input data, but a simple boosting solution for probabilistic prediction of real valued outputs is yet to be made. NGBoost is a gradient boosting approach which uses the \emph{Natural Gradient} to address technical challenges that makes generic probabilistic prediction hard with existing gradient boosting methods. Our approach is modular with respect to the choice of base learner, probability distribution, and scoring rule. We show empirically on several regression datasets that NGBoost provides competitive predictive performance of both uncertainty estimates and traditional metrics.

NGBoost是一种利用梯度提升用于概率预测的算法，可以通过考虑与目标函数相关的分布的参数并将其视为多参数提升算法的目标来泛化梯度提升以进行概率回归。它可以与任何基本学习器、任何具有连续参数的分布族以及任何得分规则一起使用，具有灵活性、可扩展性和易用性等额外优点。

NGBoost: 自然梯度提升用于概率预测