# Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective

arXiv: https://arxiv.org/abs/2412.09712 · HTML: https://mlsanity.com/paper/2412.09712/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Rashomon Capacity: A Metric for Predictive Multiplicity in Classification](https://mlsanity.com/paper/2206.01295/) (2022)
- [Predictive Multiplicity in Probabilistic Classification](https://mlsanity.com/paper/2206.01131/) (2022)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance](https://mlsanity.com/paper/2309.13775/) (2023)
- [The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression](https://mlsanity.com/paper/2202.09101/) (2022)
- [Algorithmic Arbitrariness in Content Moderation](https://mlsanity.com/paper/2402.16979/) (2024)
- [Efficient Exploration of the Rashomon Set of Rule Set Models](https://mlsanity.com/paper/2406.03059/) (2024)
- [Revisiting Rashomon: A Comment on "The Two Cultures"](https://mlsanity.com/paper/2104.02150/) (2021)

## Built on by (most cited first, 7 of 7)

- [Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML](https://mlsanity.com/paper/2507.14744/) (2025)
- [The Role of Hyperparameters in Predictive Multiplicity](https://mlsanity.com/paper/2503.13506/) (2025)
- [Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity](https://mlsanity.com/paper/2510.21303/) (2025)
- [Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective](https://mlsanity.com/paper/2601.20874/) (2026)
- [Decomposing Observational Multiplicity in Decision Trees: Leaf and Structural Regret](https://mlsanity.com/paper/2603.11701/) (2026)
- [Mitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of Classifiers](https://mlsanity.com/paper/2603.11750/) (2026)
- [Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages](https://mlsanity.com/paper/2609.36970/) (2026)

## Abstract (arXiv)

The Rashomon effect presents a significant challenge in model selection. It occurs when multiple models achieve similar performance on a dataset but produce different predictions, resulting in predictive multiplicity. This is especially problematic in high-stakes environments, where arbitrary model outcomes can have serious consequences. Traditional model selection methods prioritize accuracy and fail to address this issue. Factors such as class imbalance and irrelevant variables further complicate the situation, making it harder for models to provide trustworthy predictions. Data-centric AI approaches can mitigate these problems by prioritizing data optimization, particularly through preprocessing techniques. However, recent studies suggest preprocessing methods may inadvertently inflate predictive multiplicity. This paper investigates how data preprocessing techniques like balancing and filtering methods impact predictive multiplicity and model stability, considering the complexity of the data. We conduct the experiments on 21 real-world datasets, applying various balancing and filtering techniques, and assess the level of predictive multiplicity introduced by these methods by leveraging the Rashomon effect. Additionally, we examine how filtering techniques reduce redundancy and enhance model generalization. The findings provide insights into the relationship between balancing methods, data complexity, and predictive multiplicity, demonstrating how data-centric AI strategies can improve model performance.