Entity Embeddings of Categorical Variables (1604.06737v1)

Abstract: We map categorical variables in a function approximation problem into Euclidean spaces, which are the entity embeddings of the categorical variables. The mapping is learned by a neural network during the standard supervised training process. Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical variables. We applied it successfully in a recent Kaggle competition and were able to reach the third position with relative simple features. We further demonstrate in this paper that entity embedding helps the neural network to generalize better when the data is sparse and statistics is unknown. Thus it is especially useful for datasets with lots of high cardinality features, where other methods tend to overfit. We also demonstrate that the embeddings obtained from the trained neural network boost the performance of all tested machine learning methods considerably when used as the input features instead. As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.

• The paper demonstrates that entity embeddings map categorical variables into a continuous Euclidean space, enhancing neural network generalization on sparse datasets.
• It shows that this method significantly reduces memory usage and computation compared to traditional one-hot encoding.
• Experimental results on the Rossmann Store Sales dataset reveal a lower MAPE (0.093) for models using entity embeddings, outperforming conventional techniques.

Entity Embeddings of Categorical Variables: An Expert Analysis

This paper presents an innovative approach to handling categorical variables in neural networks through the use of entity embeddings. The authors, Cheng Guo and Felix Berkhahn, propose a method that maps categorical variables into a Euclidean space via a neural network, thus revealing the intrinsic properties of the data which can aid in the generalization of the neural network, particularly when dealing with sparse datasets and high cardinality features.

The core motivation behind the proposed methodology is to bridge a gap that exists in the applicability of neural networks to structured data, where categorical variables are prevalent. Traditional approaches like one-hot encoding often result in increased computational overhead and do not inherently accommodate the continuity assumptions of neural networks. Entity embeddings offer an alternative by learning a continuous mapping of categorical values, thereby preserving and elucidating the relationships between categories.

The paper achieves two significant outcomes: a reduction in memory usage and speed improvement over conventional approaches such as one-hot encoding; and a meaningful enhancement in the model's ability to generalize from sparse data. Furthermore, the embeddings, once learned, generalize well across different machine learning paradigms, improving their performance when used as input features.

Extensive experiments have been conducted using the Rossmann Store Sales dataset from a Kaggle competition. The authors demonstrate that neural networks augmented with entity embeddings significantly outperform various established machine learning models when dealing with unshuffled data (temporal data split). Notably, the use of entity embeddings improved the mean absolute percentage error (MAPE) to 0.093, compared to 0.101 achieved by a neural network using one-hot encoding.

The practical implications of this research are substantial. Entity embeddings provide a scalable method for handling high-dimensional categorical data efficiently, which is of paramount importance in industrial applications involving structured data, such as sales forecasting or user personalization systems.

The paper opens several avenues for future research. One intriguing line is the further exploration of the relationship between the embedding of categorical variables and finite metric spaces. This could potentially lead to a more precise understanding of embedding dimensions and inform strategies for optimizing them. Moreover, the method's potential applicability in the discretization of continuous variables for enhanced function approximation merits investigation.

Overall, this contribution to the field of machine learning provides a compelling argument for the use of entity embeddings in neural networks, addressing significant challenges in the handling of structured data. Future work on the theoretical underpinnings and expansion of application domains could further establish this methodology as a staple in the analysis of categorical data with neural networks.