More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach

As machine learning (ML) is being applied to a greater variety of practical problems, ensuring the reliability of ML is recognized as becoming increasingly important. Among several potential approaches to reliable ML, conditional selective inference (SI) is recognized as a promising approach for evaluating the statistical reliability of data-driven hypotheses selected by ML methods. The basic idea of conditional SI is to make inference on a data-driven hypothesis conditional on the selection event that the hypothesis is selected by analyzing the data with the ML algorithm. Conditional SI has been actively studied especially in the context of feature selection. Notably, Lee et al. [1] and Tibshirani et al. [2] proposed conditional SI methods for exact conditional inference on selected features by using Lasso and stepwise feature selection (SFS), respectively.