Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees

• URL: http://arxiv.org/abs/2210.11327v2
• Date: Thu, 22 Feb 2024 20:28:25 GMT
• Title: Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees
• Authors: Moacir Antonelli Ponti and Lucas de Angelis Oliveira and Mathias Esteban and Valentina Garcia and Juan Mart\'in Rom\'an and Luis Argerich
• Abstract summary: We propose a method based on metrics from training dynamics of Gradient Boosting Decision Trees (GBDTs) to assess the behavior of each training example. 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.
• Score: 1.5605040219256345
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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) 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.

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