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 data—such as rare events like luxury vehicle purchases or high-ticket service upgrades—over-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
BMC Medical Research Methodology, “Oversampling and replacement strategies in propensity score matching.” [Online]. Available: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01454-z
M. Widmann and A. Roccato, “From modeling to scoring: Correcting predicted class probabilities in imbalanced datasets.” [Online]. Available: https://www.dataversity.net/from-modeling-to-scoring-correcting-predicted-class-probabilities-in-imbalanced-datasets/
S. Rose, “Consistent estimation of propensity score functions with oversampled exposed subjects,” 2018, arXiv:1805.07684. [Online]. Available: https://arxiv.org/abs/1805.07684
K. S. Sarma, Predictive Modeling Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc., 2013
G. King and L. Zeng, “Logistic regression in rare events data,” *Political Analysis*, vol. 9, no. 2, pp. 137–163, 2001. doi: 10.1093/oxfordjournals.pan.a004868.
SAS Communities, “Why do you require adjusted probability after oversampling?” 2021. [Online]. Available: https://communities.sas.com/t5/SAS-Data-Science/Why-do-you-require-adjust-probability-after-over-sampling/td-p/752224
V. Tummalapalli, “Adjusting Propensity Model Scores During Economic Shifts: A Framework for Short-Term and Long-Term Adaptation”, IJAIBDCMS, vol. 6, no. 4, pp. 247–250, Dec. 2025, doi: 10.63282/3050-9416.IJAIBDCMS-V6I4P129.
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