The Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead

ArXi:2604.04717v1 Announce Type: cross Machine learning (ML) models have achieved strikingly high accuracies in spectroscopic classification tasks, often without a clear proof that those models used chemically meaningful features. Existing studies have linked these results to data preprocessing choices, noise sensitivity, and model complexity, but no unifying explanation is available so far. In this work, we show that these phenomena arise naturally from the intrinsic high dimensionality of spectral data.