Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research

This tutorial offers a step-by-step guide for analytical chemists to train machine learning models for MS-based metabolomics. It covers data preparation, feature engineering, model selection, evaluation, and interpretation, along with real-world examples, common pitfalls, and a complete practice dataset with code available.