教你在何时及如何变换目标变量以提升模型效果
The When and How of Target Variable Transformations
- 提出三类目标变量变换的实用场景：个体偏倚、上下文影响、普遍趋势
- 总结可操作的判断规则，帮助识别是否需要变换目标变量
- 适合数据预处理阶段想提升模型性能的研究者与工程师
机器学习流程通常包括数据收集、数据准备、模型学习和模型评估四个步骤。尽管数据准备阶段对模型准确性至关重要，但研究多聚焦于特征集的处理（如选择、变换、降维），而对目标变量变换的关注不足。实际上，目标变量变换能显著影响建模可行性，例如校正个体评分习惯差异、人口规模效应或通货膨胀等普遍趋势。本文旨在达成三个目标：首先，通过实践案例展示目标变量变换的有效性；其次，提供一系列通用‘经验法则’，帮助判断何时需要进行变换；最后，针对不同情境推荐适用的变换方法。
原文摘要 · Abstract (English)
The machine learning pipeline typically involves the iterative process of (1) collecting the data, (2) preparing the data, (3) learning a model, and (4) evaluating a model. Practitioners recognize the importance of the data preparation phase in terms of its impact on the ability to learn accurate models. In this regard, significant attention is often paid to manipulating the feature set (e.g., selection, transformations, dimensionality reduction). A point that is less well appreciated is that transformations on the target variable can also have a large impact on whether it is possible to learn a suitable model. These transformations may include accounting for subject-specific biases (e.g., in how someone uses a rating scale), contexts (e.g., population size effects), and general trends (e.g., inflation). However, this point has received a much more cursory treatment in the existing literature. The goal of this paper is three-fold. First, we aim to highlight the importance of this problem by showing when transforming the target variable has been useful in practice. Second, we will provide a set of generic ``rules of thumb'' that indicate situations when transforming the target variable may be needed. Third, we will discuss which transformations should be considered in a given situation.
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