Comparative Evaluation of Linear Regression, Cross Validation and Regularization Approaches in Multivariate Data Analysis

Author(s)

Ransford Owusu, University of MaineFollow
Felix Yeboah, University of Central FloridaFollow
Francis Effah Boateng, University of MaineFollow

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