Keywords
Cross-validation, Regularization, Ridge regression, LASSO, Elastic Net.
Abstract
This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.
STARS Citation
Owusu, Ransford; Yeboah, Felix; and Boateng, Francis Effah, "Comparative Evaluation of Linear Regression, Cross Validation and Regularization Approaches in Multivariate Data Analysis" (2025). Data Science and Data Mining. 38.
https://stars.library.ucf.edu/data-science-mining/38
Included in
Automotive Engineering Commons, Data Science Commons, Energy Systems Commons, Industrial Technology Commons, Systems Science Commons
Accessibility Statement
This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.