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.
Most read articles by the same author(s)
- Vaibhav Tummalapalli, Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 03 (2026): Volume 03 Issue 03
Similar Articles
- Vaibhav Tummalapalli, Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Elias R. Hoffmann, Predictive Behavioral Cybersecurity for Smart Healthcare and Mobile Ecosystems: An Ensemble Machine Learning Framework for Dynamic Malware Intelligence , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Tashi Wangchuk, Karma Lhendup, Data-Driven Model Supporting Defect Analysis through Vision Techniques in Press-Formed Vehicle Components , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Sara Mohammadi, A Scalable Python-Based Architecture for Causal Structure Learning in Non-Gaussian Linear Systems Using the PyCD-LiNGAM Framework , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Dr. Javier M. Ortega, Dr. Lucia Fernández-Ríos, Predictive Modeling of Online Retail Revenue Using Data Exploration and Intelligent Algorithms , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Arman V. Solberg, Prof. Elina K. Marovic, Machine Learning Approaches for Detecting Interventions and Conditions to Elevate Power Utilization in Established Facilities , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Julian E. Vance, Prof. Anya S. Petrova, Advancing Artificial Intelligence: An In-Depth Look at Machine Learning and Deep Learning Architectures, Methodologies, Applications, and Future Trends , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Dr. Alexei V. Morozov, Dr. Elena S. Petrova, Identification of Harmful Programs Using a Fusion of Deep Feature Extraction Networks and Context-Aware Sequential Modeling Techniques , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Priya Sharma, A Deep Learning-Based Personalized Recommendation Architecture for E-Commerce Using CNN-Driven Sequential Representation Learning and Temporal User Behavior Optimization , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Ahmed Z. Farouk, QUANTUM COMPUTATIONAL AND MACHINE LEARNING PARADIGMS FOR FINANCIAL OPTIMIZATION, RISK MANAGEMENT, AND DATA DIVERSITY: A COMPREHENSIVE THEORETICAL SYNTHESIS , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 02 (2026): Volume 03 Issue 02
1-10 of 41
Next
You may also start an advanced similarity search for this article.