Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees

TL;DR

This paper introduces a novel method using training dynamics of Gradient Boosting Decision Trees to identify and mitigate noisy labels, enhancing data quality and model performance on structured datasets.

Contribution

The paper presents a new approach leveraging training dynamics metrics of GBDTs to detect noisy labels and improve data quality, outperforming existing methods.

Findings

Effective detection of noisy labels in structured datasets

Improved model metrics after data cleaning

Successful deployment in an industry case

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.