Performance Evaluation and Comparison of a New Regression Algorithm

Sabina Gooljar, Kris Manohar, Patrick Hosein

2023

Abstract

In recent years, Machine Learning algorithms, in particular supervised learning techniques, have been shown to be very effective in solving regression problems. We compare the performance of a newly proposed regression algorithm against four conventional machine learning algorithms namely, Decision Trees, Random Forest, k-Nearest Neighbours and XG Boost. The proposed algorithm was presented in detail in a previous paper but detailed comparisons were not included. We do an in-depth comparison, using the Mean Absolute Error (MAE) as the performance metric, on a diverse set of datasets to illustrate the great potential and robustness of the proposed approach. The reader is free to replicate our results since we have provided the source code in a GitHub repository while the datasets are publicly available.

Paper Citation

in Harvard Style

Gooljar S., Manohar K. and Hosein P. (2023). Performance Evaluation and Comparison of a New Regression Algorithm. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 524-531. DOI: 10.5220/0012135400003541

in Bibtex Style

@conference{data23,
author={Sabina Gooljar and Kris Manohar and Patrick Hosein},
title={Performance Evaluation and Comparison of a New Regression Algorithm},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={524-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012135400003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Performance Evaluation and Comparison of a New Regression Algorithm
SN - 978-989-758-664-4
AU - Gooljar S.
AU - Manohar K.
AU - Hosein P.
PY - 2023
SP - 524
EP - 531
DO - 10.5220/0012135400003541
PB - SciTePress