12种归一化方法在16个数据集上测试，揭示不同模型对特征缩放的依赖程度。
The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks
- 系统评估12种缩放方法在14种算法上的表现差异。
- 集成模型如XGBoost对缩放不敏感，而MLP、SVM等显著依赖缩放方式。
- 提供可复现代码和结果，帮助从业者选择最优缩放策略。
本研究针对特征缩放缺乏全面研究的问题，系统评估了12种缩放技术（包括若干不常见变换）在14种机器学习算法及16个数据集上的表现，涵盖分类与回归任务。通过准确率、MAE、MSE和$R^2$等指标分析预测性能，同时测量训练时间、推理时间和内存使用量。关键发现表明，尽管集成方法（如随机森林、梯度提升模型XGBoost、CatBoost、LightGBM）表现出较强的鲁棒性，基本不受缩放影响，但逻辑回归、SVM、TabNet和MLPs等模型则显著依赖所选缩放器。该大规模实证分析所有源码、实验结果与模型参数均公开，确保完全透明与可复现，为实践者提供了面向模型的特征缩放选择重要指导。
原文摘要 · Abstract (English)
This research addresses the critical lack of comprehensive studies on feature scaling by systematically evaluating 12 scaling techniques - including several less common transformations - across 14 different Machine Learning algorithms and 16 datasets for classification and regression tasks. We meticulously analyzed impacts on predictive performance (using metrics such as accuracy, MAE, MSE, and $R^2$) and computational costs (training time, inference time, and memory usage). Key findings reveal that while ensemble methods (such as Random Forest and gradient boosting models like XGBoost, CatBoost and LightGBM) demonstrate robust performance largely independent of scaling, other widely used models such as Logistic Regression, SVMs, TabNet, and MLPs show significant performance variations highly dependent on the chosen scaler. This extensive empirical analysis, with all source code, experimental results, and model parameters made publicly available to ensure complete transparency and reproducibility, offers model-specific crucial guidance to practitioners on the need for an optimal selection of feature scaling techniques.
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