2024/06/27 by Tomohiro Shiraishi, Shiraishi, Tomohiro, Tatsuya Matsukawa +5 · 2 citations
Engineering · Mathematics · #Advanced Statistical Methods and Models #Artificial intelligence #Computer science #Data mining #Econometrics #FOS: Computer and information sciences #Fault Detection and Control Systems #Geology #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Pipeline (software) #Programming language #Statistical hypothesis testing #Statistical inference #Statistics #Test (biology)
paper · pdf · doi:10.48550/arxiv.2406.18902
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/06/27 · openalex created_date 2024/07/01 · openalex updated_date 2026/08/01
A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating various analysis algorithms. In this paper, we propose a novel statistical test to assess the significance of data analysis pipelines in feature selection problems. Our approach enables the systematic development of valid statistical tests applicable to any feature selection pipeline composed of predefined components. We develop this framework based on selective inference, a statistical technique that has recently gained attention for data-driven hypotheses. As a proof of concept, we consider feature selection pipelines for linear models, composed of three missing value imputation algorithms, three outlier detection algorithms, and three feature selection algorithms. We theoretically prove that our statistical test can control the probability of false positive feature selection at any desired level, and demonstrate its validity and effectiveness through experiments on synthetic and real data. Additionally, we present an implementation framework that facilitates testing across any configuration of these feature selection pipelines without extra implementation costs.