Embeddings and Attention in Predictive Modeling

Kevin Kuo and Ronald Richman

Papers from arXiv.org

Abstract: 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.

Date: 2021-04
New Economics Papers: this item is included in nep-big and nep-cmp
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