The Impact of Balancing Methods on Predictive Multiplicity in Imbalanced Classification

Core Concepts

Balancing methods used to address imbalanced datasets can inflate the predictive multiplicity in the Rashomon set, leading to increased risks in model selection.

Abstract

The study investigates the impact of common balancing methods (random oversampling, SMOTE, random undersampling, and near miss) on the Rashomon effect in imbalanced classification tasks. The key findings are: Balancing methods increase the ambiguity and discrepancy of the Rashomon set, indicating higher predictive multiplicity compared to the original imbalanced dataset. The variable importance order discrepancy, a measure of model behavior change, does not show statistically significant differences between the Rashomon sets of original and balanced datasets. Partial resampling, proposed as a solution to mitigate the bias of balancing methods, does not effectively address the increased predictive multiplicity. The extended performance-gain plot, which monitors the trade-off between performance gain and Rashomon metrics, is proposed as a tool to responsibly conduct the model selection process when using balancing methods. The results highlight the importance of considering the Rashomon effect and predictive multiplicity when applying balancing methods in imbalanced classification problems. Blindly selecting a model from the Rashomon set can lead to serious consequences, as the models may yield conflicting predictions for the same samples. The proposed performance-gain plot for Rashomon metrics can help researchers and practitioners make informed decisions during the model selection process.

Stats

The imbalanced ratio (majority class samples / minority class samples) of the datasets varies between 1.54 and 129.53. The Rashomon parameter ε is set to 0.05. The resampling ratios (imbalanced ratio after balancing) considered are {1, 1.05, 1.10, 1.15, 1.20, 1.25}.

Quotes

"Balancing methods inflate the predictive multiplicity, and they yield varying results." "The extended performance-gain plot for the Rashomon effect can be a solution to monitor the trade-off between performance gain and multiplicity."

Key Insights Distilled From

An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification

by Must... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01557.pdf

Deeper Inquiries

How does the complexity of the imbalanced dataset, such as class overlap or small disjuncts, affect the Rashomon effect of balancing methods?

The complexity of an imbalanced dataset, characterized by factors like class overlap or small disjuncts, can significantly impact the Rashomon effect of balancing methods. Class overlap refers to instances where the feature distributions of different classes overlap, making it challenging for the model to distinguish between them accurately. In such cases, balancing methods may struggle to address the imbalance effectively, leading to an increase in predictive multiplicity within the Rashomon set. Similarly, small disjuncts, which are subsets of data with distinct characteristics, can further exacerbate the challenges posed by imbalanced datasets. Balancing methods may inadvertently amplify the differences between these small disjuncts, resulting in conflicting model predictions and higher levels of multiplicity. This can make it harder to select an appropriate model from the Rashomon set, as the models may exhibit varying behaviors based on these intricate data patterns. In essence, the complexity of imbalanced datasets, including class overlap and small disjuncts, can magnify the Rashomon effect by introducing additional layers of uncertainty and variability in model predictions. Balancing methods must be carefully applied and evaluated in such scenarios to mitigate the impact of these complexities on model behavior and selection.

Can cost-sensitive methods, such as class-weighted loss functions, mitigate the impact of balancing methods on the Rashomon effect?

Cost-sensitive methods, like class-weighted loss functions, have the potential to mitigate the impact of balancing methods on the Rashomon effect to some extent. These methods assign different costs or weights to different classes based on their importance or rarity, aiming to address the imbalance in the dataset effectively during model training. By incorporating class weights into the loss function, models can prioritize the minority class instances, thereby reducing the bias towards the majority class that often leads to low performance in imbalanced classification tasks. This adjustment can help in improving the predictive accuracy for the minority class and potentially reduce the multiplicity within the Rashomon set. However, while cost-sensitive methods can contribute to balancing the impact of imbalanced datasets, they may not completely eliminate the challenges associated with the Rashomon effect. The effectiveness of these methods in mitigating the impact of balancing methods on multiplicity depends on the specific characteristics of the dataset and the chosen approach to assigning class weights. In conclusion, while cost-sensitive methods like class-weighted loss functions can be valuable in addressing imbalanced datasets, their ability to mitigate the Rashomon effect of balancing methods may vary based on the dataset's complexity and the chosen weighting strategy.

What other model behavior metrics, beyond variable importance order, could be used to further investigate the Rashomon effect of balancing methods?

In addition to variable importance order, several other model behavior metrics can be leveraged to further investigate the Rashomon effect of balancing methods in imbalanced classification tasks. Some of these metrics include: Feature Interaction Analysis: Examining how features interact with each other in the model can provide insights into how balancing methods impact the relationships between variables and their influence on predictions. Model Calibration: Assessing the calibration of the model predictions can reveal how well the model's confidence estimates align with the actual outcomes, shedding light on the reliability of predictions across different classes. Decision Boundary Analysis: Analyzing the decision boundaries of the model can help understand how balancing methods affect the model's ability to separate classes, especially in regions of class overlap or small disjuncts. Model Robustness: Evaluating the robustness of the model to perturbations in the input data can indicate how sensitive the model is to changes and how balancing methods impact its stability and generalization capabilities. Prediction Consistency: Investigating the consistency of predictions across different models within the Rashomon set can highlight the extent of multiplicity and the variability in model outputs, providing insights into the reliability of model selection. By incorporating these additional model behavior metrics into the analysis of the Rashomon effect, researchers can gain a more comprehensive understanding of how balancing methods influence model behavior and selection in the context of imbalanced datasets.