Feature Engineering

Feature engineering is the process of transforming raw text into numerical representations that capture the information most useful for a given task. In classical NLP, it encompasses decisions about tokenization, n-gram ranges, TF-IDF weighting, POS-tag inclusion, entity type indicators, and character-level features. Good feature engineering required deep domain knowledge and could dominate model accuracy; one of the major shifts brought by deep learning is that end-to-end models learn useful representations without manual feature design.