Embeddings and Attention in Predictive Modeling

We explore in depth how categorical data can be processed with embeddings in the context of claim severity modeling. We develop several models that range in complexity from simple neural networks to state-of-the-art attention-based architectures that utilize embeddings. We illustrate the utility of learned embeddings from neural networks as pretrained features in generalized linear models and discuss methods for visualizing and interpreting embeddings. Finally, we explore how attention-based models can contextually augment embeddings, leading to enhanced predictive performance.