Embeddings and features are both used to represent data in machine learning, but they differ in how they’re created and used. Features are measurable properties or characteristics of data that serve as inputs to a model. These can be raw values (like pixel intensities in an image) or engineered attributes (like statistical summaries or domain-specific metrics). For example, in text classification, features might include word counts, term frequency-inverse document frequency (TF-IDF) scores, or syntactic tags. Features are often handcrafted based on domain knowledge to highlight patterns relevant to a task, such as using edge detection filters in image processing to emphasize object boundaries.
Embeddings, on the other hand, are learned representations of data, typically in a lower-dimensional space. Instead of relying on explicit human design, embeddings are generated by training a model to capture relationships in the data. For instance, word embeddings like Word2Vec or BERT convert words into dense vectors where semantically similar words (e.g., “king” and “queen”) are closer in vector space. Similarly, image embeddings from models like ResNet encode images into vectors that abstract visual features like shapes or textures. These embeddings are not directly interpretable but distill meaningful patterns useful for downstream tasks like classification or clustering.
The key distinction lies in their creation and purpose. Features are often manually defined or derived from domain expertise, making them interpretable but potentially limited in capturing complex relationships. Embeddings automate feature extraction by learning latent patterns, which can handle high-dimensional or unstructured data more effectively. For example, instead of engineering features for a recommendation system (e.g., user age or product category), embeddings can represent users and items as vectors learned from interaction data. However, embeddings require sufficient training data and computational resources, and their lack of transparency can make debugging harder. Choosing between them depends on the problem: features work well for structured, interpretable scenarios, while embeddings excel at handling unstructured data or tasks where manual feature engineering is impractical.