Real world datasets contain incorrectly labeled instances that hamper the performance of the model and, in particular, the ability to generalize out of distribution. Also, each example might have different contribution towards learning. This motivates studies to better understanding of the role of data instances with respect to their contribution in good metrics in models. In this paper we propose a method based on metrics computed from training dynamics of Gradient Boosting Decision Trees (GBDTs) to assess the behavior of each training example. We focus on datasets containing mostly tabular or structured data, for which the use of Decision Trees ensembles are still the state-of-the-art in terms of performance. Our methods achieved the best results overall when compared with confident learning, direct heuristics and a robust boosting algorithm. We show results on detecting noisy labels in order clean datasets, improving models' metrics in synthetic and real public datasets, as well as on a industry case in which we deployed a model based on the proposed solution.

翻译：现实世界的数据集包含错误标注的样本，这损害了模型的性能，尤其是其在分布外泛化的能力。此外，每个样本对学习的贡献可能有所不同。这促使研究更好地理解数据实例在模型良好指标中的贡献作用。本文提出一种基于梯度提升决策树（GBDT）训练动态计算指标的方法，用于评估每个训练样本的行为。我们主要关注包含表格或结构化数据的数据集，此类数据中决策树集成模型在性能上仍属当前最优。与置信学习、直接启发式方法和鲁棒提升算法相比，我们的方法在整体上取得了最佳结果。我们展示了在清理数据集中检测噪声标签、提升合成与真实公开数据集模型指标的效果，以及一个基于所提方案部署模型的工业案例。