基于机器学习的预测建模用于提高葡萄酒质量.
Khushboo Jain1, Keshav Kaushik1, Sachin Kumar Gupta2
1School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Scientific reports
|October 9, 2023
概括
机器学习模型可以准确预测葡萄酒的质量. 随机森林和极端梯度提升实现了高精度,XGBoost使用关键的物理化学特征达到100%.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 食品科学 食品科学 食品科学
背景情况:
- 葡萄酒质量认证对葡萄酒行业至关重要.
- 准确的葡萄酒质量预测有助于行业标准和消费者信任.
- 物理化学特性显著影响葡萄酒的质量.
研究的目的:
- 开发和评估用于葡萄酒质量预测的机器学习模型.
- 确定影响葡萄酒质量的关键生理化学特征.
- 通过特征选择和分析优化预测模型.
主要方法:
- 使用了红葡萄酒数据集 (RWD) 的11种生理化学性质.
- 训练并测试了五种机器学习模型,重点是随机森林 (RF) 和极端梯度增强 (XGBoost).
- 采用特征选择技术,包括集群分析,以确定基本预测因素并解决对线性问题.
主要成果:
- 随机森林和XGBoost在测试模型中表现出卓越的性能.
- 在训练和测试关键属性时,XGBoost在预测葡萄酒质量方面实现了100%的准确性.
- 特性重要性分析确定了关键的生理化学性质,以准确预测.
结论:
- 机器学习模型,特别是XGBoost和RF,是葡萄酒质量预测的有效工具.
- 特性选择显著提高了模型的准确性和效率.
- 识别和利用关键的生理化学特性对于精确的葡萄酒质量预测至关重要.
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