为自动特征工程生成的特征提供可信赖的统计检验方法
Statistical Test for Auto Feature Engineering by Selective Inference
- 基于选择性推断框架，对自动特征工程生成的特征进行统计检验
- 可输出p值，严格控制虚假发现风险，理论保障可靠
- 适合关注模型可解释性与特征可靠性研究的从业者
自动特征工程（AFE）通过数据驱动方式将原始数据转换为提升模型性能的有意义特征，在实际机器学习流程中至关重要。然而，由于AFE通常被建模为组合搜索问题并用启发式算法求解，其生成特征的可靠性难以理论量化。为此，本文提出一种基于选择性推断框架的新型统计检验方法，以评估树搜索类启发式AFE算法生成的特征在线性模型中的表现。该方法能输出具有统计显著性的p值，实现对虚假发现风险的理论控制，为特征可靠性提供可信赖的评估手段。
原文摘要 · Abstract (English)
Auto Feature Engineering (AFE) plays a crucial role in developing practical machine learning pipelines by automating the transformation of raw data into meaningful features that enhance model performance. By generating features in a data-driven manner, AFE enables the discovery of important features that may not be apparent through human experience or intuition. On the other hand, since AFE generates features based on data, there is a risk that these features may be overly adapted to the data, making it essential to assess their reliability appropriately. Unfortunately, because most AFE problems are formulated as combinatorial search problems and solved by heuristic algorithms, it has been challenging to theoretically quantify the reliability of generated features. To address this issue, we propose a new statistical test for generated features by AFE algorithms based on a framework called selective inference. As a proof of concept, we consider a simple class of tree search-based heuristic AFE algorithms, and consider the problem of testing the generated features when they are used in a linear model. The proposed test can quantify the statistical significance of the generated features in the form of $p$-values, enabling theoretically guaranteed control of the risk of false findings.
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