用机器学习预测咖啡评分，关键在特征筛选和参数调优
Prediction of Coffee Ratings Based On Influential Attributes Using SelectKBest and Optimal Hyperparameters
- 用TF-IDF提取文本特征，再通过SelectKBest选关键属性
- 集成学习模型（如XGBoost）F1、AUC等指标均优于基础模型
- 适合想用数据辅助品鉴的咖啡产业或研究者参考
本研究利用监督学习算法，基于用户评论中的文本与数值属性预测咖啡评分。通过文本清洗、TF-IDF特征提取及SelectKBest特征选择进行数据预处理。训练并评估了六种模型：决策树、K近邻、多层感知机、随机森林、额外树和XGBoost，均采用优化后的超参数。模型性能以F1分数、几何平均数（Gmean）和AUC为主要评估指标。结果表明，集成方法（额外树、随机森林、XGBoost）以及多层感知机在各项指标上均显著优于决策树和K近邻等简单分类器。研究强调了严谨的特征选择与超参数调优对构建可靠感官产品预测系统的重要性，为传统专业品鉴提供了数据驱动的补充方案。
原文摘要 · Abstract (English)
This study explores the application of supervised machine learning algorithms to predict coffee ratings based on a combination of influential textual and numerical attributes extracted from user reviews. Through careful data preprocessing including text cleaning, feature extraction using TF-IDF, and selection with SelectKBest, the study identifies key factors contributing to coffee quality assessments. Six models (Decision Tree, KNearest Neighbors, Multi-layer Perceptron, Random Forest, Extra Trees, and XGBoost) were trained and evaluated using optimized hyperparameters. Model performance was assessed primarily using F1-score, Gmean, and AUC metrics. Results demonstrate that ensemble methods (Extra Trees, Random Forest, and XGBoost), as well as Multi-layer Perceptron, consistently outperform simpler classifiers (Decision Trees and K-Nearest Neighbors) in terms of evaluation metrics such as F1 scores, G-mean and AUC. The findings highlight the essence of rigorous feature selection and hyperparameter tuning in building robust predictive systems for sensory product evaluation, offering a data driven approach to complement traditional coffee cupping by expertise of trained professionals.
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