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Embeddings and Attention in Predictive Modeling

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  • Kevin Kuo
  • Ronald Richman

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.

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  • Kevin Kuo & Ronald Richman, 2021. "Embeddings and Attention in Predictive Modeling," Papers 2104.03545, arXiv.org.
    • Handle: RePEc:arx:papers:2104.03545
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    Cited by:

    1. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Aug 2024.
    2. Ronald Richman & Mario V. Wuthrich, 2021. "LocalGLMnet: interpretable deep learning for tabular data," Papers 2107.11059, arXiv.org.
    3. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
    4. Ronald Richman & Salvatore Scognamiglio & Mario V. Wuthrich, 2024. "The Credibility Transformer," Papers 2409.16653, arXiv.org.

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