Entity embedding is the process of vector representation based on the interrelationship among the different values in the categorical variables continuously to make their learning process easier, facilitate the training model, and boost performance. Entity embedding performs better than one-hot encoding. Entity embedding provides the inherent properties of categorical variables by mapping the relevant values close to each other in the embedding space.

The significance of entity embeddings is memory usage reduction, speeding up the learning process, and high spot unknown relationship between the categories of the variables. Entity embedding on structured data provides great results that do not require feature engineering or domain-specific knowledge. Entity embedding are applied in real-world problems such as natural language processing and forecasting problems.

  • Entity embeddings, a method to embed categorical variables in real-valued vector spaces and map related values closer together in embedding space, reveal the inherent continuity of the data.

  • It continuously represents categorical variables, retaining the relationship between different data values and facilitating the models training. 

  • In the context of neural networks, the entity embedding method defines a distance measure for categorical variables; it can be used for visualizing categorical data and for data clustering.

  • Entity embedding facilitates the neural network to generalize better when the data is sparse and statistics are unknown.

  • The key benefit of entity embedding is that it reduces the training speed of the neural network and memory usage.