提出基于MAE准则的分类特征二分分裂新算法，解决传统编码失效问题。
Binary Split Categorical feature with Mean Absolute Error Criteria in CART
- 设计无需数值编码的分类特征二分分裂方法
- 证明无监督编码在MAE下不可行
- 适合需精准回归的树模型场景
在分类与回归树（CART）算法中，使用GINI和熵等标准准则对分类特征进行高效分裂已得到充分研究。然而，传统上采用数值编码方法来应用均值绝对误差（MAE）准则处理分类特征。本文表明，无监督数值编码方法在MAE准则下不可行。此外，我们提出一种新颖且高效的分裂算法，解决了使用MAE准则处理分类特征的挑战。研究结果揭示了现有方法的局限性，并为提升CART算法中分类数据的处理能力提供了有前景的解决方案。
原文摘要 · Abstract (English)
In the context of the Classification and Regression Trees (CART) algorithm, the efficient splitting of categorical features using standard criteria like GINI and Entropy is well-established. However, using the Mean Absolute Error (MAE) criterion for categorical features has traditionally relied on various numerical encoding methods. This paper demonstrates that unsupervised numerical encoding methods are not viable for the MAE criteria. Furthermore, we present a novel and efficient splitting algorithm that addresses the challenges of handling categorical features with the MAE criterion. Our findings underscore the limitations of existing approaches and offer a promising solution to enhance the handling of categorical data in CART algorithms.
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