# To SMOTE, or not to SMOTE?

arXiv: https://arxiv.org/abs/2201.08528 · HTML: https://mlsanity.com/paper/2201.08528/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Logistic Regression: Tight Bounds for Stochastic and Online Optimization](https://mlsanity.com/paper/1405.3843/) (2014)

## Built on by (most cited first, 14 of 14)

- [Position: Why We Must Rethink Empirical Research in Machine Learning](https://mlsanity.com/paper/2405.02200/) (2024)
- [CTSyn: A Foundation Model for Cross Tabular Data Generation](https://mlsanity.com/paper/2406.04619/) (2024)
- [On the Usefulness of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2306.15636/) (2023)
- [An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification](https://mlsanity.com/paper/2405.01557/) (2024)
- [Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification](https://mlsanity.com/paper/2409.19751/) (2024)
- [Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants](https://mlsanity.com/paper/2402.03819/) (2024)
- [Supervised Learning via Ensembles of Diverse Functional Representations: the Functional Voting Classifier](https://mlsanity.com/paper/2403.15778/) (2024)
- [Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems](https://mlsanity.com/paper/2501.01441/) (2024)
- [Gender Fairness of Machine Learning Algorithms for Pain Detection](https://mlsanity.com/paper/2506.11132/) (2025)
- [Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk](https://mlsanity.com/paper/2407.11089/) (2024)
- [Certified Interpolation Oversampling: Per-Instance Safety Guarantees for Imbalanced Learning](https://mlsanity.com/paper/2501.15790/) (2025)
- [Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework](https://mlsanity.com/paper/2311.08080/) (2023)
- [Skew-Probabilistic Neural Networks for Learning from Imbalanced Data](https://mlsanity.com/paper/2312.05878/) (2023)
- [Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering](https://mlsanity.com/paper/2507.10088/) (2025)

## Abstract (arXiv)

Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples. While it is well known that balancing affects each classifier differently, most prior empirical studies did not include strong state-of-the-art (SOTA) classifiers as baselines. In this work, we are interested in understanding whether balancing is beneficial, particularly in the context of SOTA classifiers. Thus, we conduct extensive experiments considering three SOTA classifiers along the weaker learners used in previous investigations. Additionally, we carefully discern proper metrics, consistent and non-consistent algorithms and hyper-parameter selection methods and show that these have a significant impact on prediction quality and on the effectiveness of balancing. Our results support the known utility of balancing for weak classifiers. However, we find that balancing does not improve prediction performance for the strong ones. We further identify several other scenarios for which balancing is effective and observe that prior studies demonstrated the utility of balancing by focusing on these settings.