# Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering

arXiv: https://arxiv.org/abs/2202.06146 · HTML: https://mlsanity.com/paper/2202.06146/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 91 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Less is More: Minimizing Code Reorganization using XTREE](https://mlsanity.com/paper/1609.03614/) (2016)

## Built on by (most cited first, 6 of 6)

- [Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework](https://mlsanity.com/paper/2311.18460/) (2023)
- [VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs](https://mlsanity.com/paper/2408.04125/) (2024)
- [A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models](https://mlsanity.com/paper/2409.15228/) (2024)
- [DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks](https://mlsanity.com/paper/2410.19550/) (2024)
- [A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection](https://mlsanity.com/paper/2504.15632/) (2025)
- [On The Impact of Merge Request Deviations on Code Review Practices](https://mlsanity.com/paper/2506.08860/) (2025)

## Abstract (arXiv)

Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.