This paper details the approach of the team $\textit{Kohrrelation}$ in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine learning context with theoretically justified loss functions for gradient boosting. We devise a spatial cross-validation scheme and show that in our setting it provides a better proxy for test set performance than naive cross-validation. The predictions are benchmarked against boosting approaches with different loss functions, and perform competitively in terms of the score criterion, finally placing second in the competition ranking.

翻译：本文详细介绍了2021年极端价值分析数据挑战中团队 $\ textit{Kohrrelation} 的方法, 涉及对毗连美国的野火数量和大小的预测。 我们的方法在机器学习中使用了极端价值理论的理念, 机体学习中具有理论上合理的梯度推升损失函数。 我们设计了一个空间交叉校验计划, 并显示在我们的设置中, 它比天真的交叉校验为测试设定性能提供了更好的替代物。 这些预测是参照具有不同损失功能的加速方法, 并在评分标准上具有竞争力, 最后在竞争排名中排在第二。