Tabular machine learning

Tabular machine learning covers classification and regression tasks on structured tables (rows = examples, columns = features). Historically dominated by tree-based algorithms (XGBoost, random forests, AdaBoost), it required tedious hyperparameter tuning and feature engineering. Tabular foundation models (like TabFM) bring the zero-shot / in-context learning paradigm from LLMs to this domain.