N-gram Features Bridge NLP and Tabular ML: How Feature Engineering Beat 99% of Competitors

Traditional machine learning treats categorical features independently, missing the interaction patterns that drive customer behavior. By applying NLP-inspired n-gram techniques to categorical combinations, this approach captures hidden signals standard models overlook, transforming middling accuracy into top-tier performance. For practitioners working with categorical-heavy datasets, this demonstrates that breakthrough improvements come from creative feature representation rather than model sophistication alone.