What are Embeddings in Feature Engineering?
In the context of feature engineering within machine learning and artificial intelligence, embeddings refer to a technique for mapping high-dimensional data into a lower-dimensional space. This is particularly useful for transforming categorical variables into a format more suitable for machine learning algorithms. By representing data as dense vectors in a continuous vector space, embeddings capture semantic relationships between different data points.
One of the most common applications of embeddings is in natural language processing (NLP), where words or phrases are represented as vectors. Techniques like Word2Vec and GloVe leverage vast amounts of text data to learn vector representations of words that reflect their meanings. For instance, words with similar meanings will have vectors that are close together in this embedding space.
Beyond text, embeddings can also represent images, user preferences, and other complex data types, thereby allowing models to generalize better and improve predictive accuracy. By using embeddings, data scientists can reduce the dimensionality of their datasets while retaining critical information, making them a powerful tool in the feature engineering process.