Open Access
A Framework for Adjusting Oversampling Bias in Machine Learning Models
4
Independent Researcher, Atlanta, USA
Abstract
Predictive modeling in the automotive industry often involves analyzing customer behavior to anticipate events such as vehicle purchases, service visits, or campaign responses. However, when working with imbalanced dataâsuch as rare events like luxury vehicle purchases or high-ticket service upgradesâover-sampling techniques are commonly used. These techniques introduce bias into the sample, requiring adjustments to predicted probabilities to reflect the true population proportions. This paper explores the methodology of adjusting predicted probabilities using prior probabilities and demonstrates its application in automotive propensity models.
Keywords
Propensity modeling,
Machine Learning,
Prior Probabilities,
Oversampling Bias,
Adjusted Probability,
Automotive
References
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S. Rose, âConsistent estimation of propensity score functions with oversampled exposed subjects,â 2018, arXiv:1805.07684. [Online]. Available: https://arxiv.org/abs/1805.07684
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