Embeddings vs One-Hot Tradeoffs: Making the Right Choice for Categorical Data
When working with categorical data in machine learning, one of the most consequential decisions you’ll make is how to represent these variables numerically. Two dominant approaches—one-hot encoding and embeddings—offer vastly different trade-offs in terms of dimensionality, computational efficiency, semantic representation, and model performance. While one-hot encoding has served as the traditional go-to method for decades, … Read more