10 Ways to Use Embeddings for Tabular ML Tasks

Embeddings, traditionally used in NLP, can enhance tabular machine learning workflows in multiple ways. Ten strategies are presented: encoding high-cardinality categorical features, averaging word embeddings for text columns, clustering embeddings into meta-features, learning self-supervised tabular embeddings through masked prediction or perturbation detection, building multi-labeled categorical embeddings, using contextual embeddings with self-attention, learning embeddings on binned numerical features, fusing embeddings with raw features, applying sentence transformers for long text, and feeding embeddings into tree-based models. These techniques unlock semantic similarity, enable richer feature interactions, and produce compact representations that improve model performance beyond traditional encoding methods.