Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees

arXiv:2210.11327v2 Announce Type: replace
Abstract: 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) …

abstract arxiv boosting cs.lg data data quality datasets decision decision trees distribution dynamics example gradient instances performance quality role stat.ml studies training trees type understanding world