Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging
This paper introduces an integrated framework combining Oblique Random Forests with a novel permutation-based feature importance algorithm (NEOFIT) to achieve statistically valid, interpretable sex classification from brain imaging data across humans and macaques, overcoming the limitations of traditional methods in handling high-dimensional neuroimaging features.