Red Wine Prediction Comparing Several Machine Learning Models

Yitong Huang

2024

Abstract

With the increasing interest in red wine among consumers, the quality of red wine has become a topic of significant importance. However, the limited availability of trained wine tasters in certain regions, especially for red wine, has hindered the progress of the red wine industry. Therefore, the utilization of mathematical models and computer software for assessing the quality, identification, and classification of red wine is crucial.The research articles primarily focus on comparing the accuracy of various methods such as Random Forest, Radial Basis Function, and Naive Bayes models. Among these models, the Random Forest method demonstrates the highest accuracy, with an adjusted of 0.8656. The study also investigates the factors influencing the model’s accuracy and proposes optimization strategies using NBF_NB and Genetic algorithms for enhanced precision in the results. As the demand for high-quality red wine continues to grow, the implementation of advanced analytical techniques and optimization methods is imperative for ensuring accurate and efficient wine quality evaluation.

Paper Citation

in Harvard Style

Huang Y. (2024). Red Wine Prediction Comparing Several Machine Learning Models. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 169-173. DOI: 10.5220/0013007200004601

in Bibtex Style

@conference{iampa24,
author={Yitong Huang},
title={Red Wine Prediction Comparing Several Machine Learning Models},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={169-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013007200004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Red Wine Prediction Comparing Several Machine Learning Models
SN - 978-989-758-722-1
AU - Huang Y.
PY - 2024
SP - 169
EP - 173
DO - 10.5220/0013007200004601
PB - SciTePress