International Journal of Research and Scientific Innovation (IJRSI)
A Comprehensive Comparative Study of Classification and Regression Architectures: Empirical Performance Benchmarking on Standardized Datasets
Published May 13, 2026 • Vol. 13, Issue 4, pp. 2094–2097Open Access
DOI: 10.51244/IJRSI.2026.1304000178
Abstract
Supervised learning remains the backbone of predictive analytics. However, the decision to treat a target variable as continuous (Regression) or categorical (Classification) significantly alters model behavior and utility. This paper provides an exhaustive comparison of five classification and five regression techniques. Using the Wine Quality Dataset, we apply identical feature engineering to both paradigms. We measure performance through Mean Squared Error ($MSE$), $R^2$, Accuracy, and F1-Score. The results demonstrate that ensemble methods, specifically Random Forest and XGBoost, consistently outperform linear and kernel-based models, though classification provides a more robust framework for noisy data environments.
Keywords: Supervised learning, regression, classification
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 13, Issue 4 |
| Pages | 2094–2097 |
| Publication date | May 13, 2026 |
| DOI | 10.51244/IJRSI.2026.1304000178 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Dr. Het Trivedi, & Mrs. Komal Shukla (2026). A Comprehensive Comparative Study of Classification and Regression Architectures: Empirical Performance Benchmarking on Standardized Datasets. International Journal of Research and Scientific Innovation (IJRSI), 13(4), 2094-2097. https://doi.org/10.51244/IJRSI.2026.1304000178
BibTeX
@article{Dr2026,
title = {A Comprehensive Comparative Study of Classification and Regression Architectures: Empirical Performance Benchmarking on Standardized Datasets},
author = {Dr. Het Trivedi and Mrs. Komal Shukla},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {13},
number = {4},
pages = {2094--2097},
year = {2026},
doi = {10.51244/IJRSI.2026.1304000178},
publisher = {RSIS International}
}