Augmentation#
Simulate real-world variations in your spectral data to enhance model robustness. This can include introducing controlled noise, baseline shifts, or small wavelength misalignments to mimic real measurement conditions. By doing so, your model learns to handle variability in experimental setups, instrument performance, and sample preparation—leading to more reliable predictions when applied to unseen data.