新编码方法让高基数类别变量更高效，提升模型速度与可解释性。
Efficient Representations for High-Cardinality Categorical Variables in Machine Learning
- 用均值编码、低秩编码等生成紧凑类别嵌入
- 在多数据集上显著提升模型性能与计算效率
- 适合处理大规模数据的机器学习场景
高基数类别变量在机器学习中带来计算效率和模型可解释性的挑战。传统独热编码常导致高维稀疏特征空间，增加过拟合风险并降低可扩展性。本文提出均值编码、低秩编码及多项式逻辑回归编码等新型编码技术，利用充分表示生成类别数据的紧凑且信息丰富的嵌入。我们在多种数据集上进行了严格的理论分析与实证验证，结果表明相比基线方法，所提技术在模型性能和计算效率方面均有显著提升。这些方法在需要大规模数据可扩展解决方案的领域尤为有效，为机器学习应用提供了更稳健高效的路径。
原文摘要 · Abstract (English)
High\-cardinality categorical variables pose significant challenges in machine learning, particularly in terms of computational efficiency and model interpretability. Traditional one\-hot encoding often results in high\-dimensional sparse feature spaces, increasing the risk of overfitting and reducing scalability. This paper introduces novel encoding techniques, including means encoding, low\-rank encoding, and multinomial logistic regression encoding, to address these challenges. These methods leverage sufficient representations to generate compact and informative embeddings of categorical data. We conduct rigorous theoretical analyses and empirical validations on diverse datasets, demonstrating significant improvements in model performance and computational efficiency compared to baseline methods. The proposed techniques are particularly effective in domains requiring scalable solutions for large datasets, paving the way for more robust and efficient applications in machine learning.
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