From Uncontextualized Embeddings to Marginal Feature Effects: Incorporating Intelligibility into Tabular Transformer Networks
Keywords: Tabular Deep Learning, Interpretability, Tabular Transformer Networks
Abstract: In recent years, deep neural networks have showcased their predictive power across a variety of tasks. The transformer architecture, originally developed for natural language processing, has also shown great efficiency in handling tabular data, offering a competitive alternative to traditional gradient-boosted decision trees in this domain. However, this predictive power comes at the cost of intelligibility: Marginal feature effects are almost completely lost in the black-box nature of deep tabular transformer networks. Alternative architectures that use the additivity constraints of classical statistical regression models can maintain intelligible marginal feature effects, but often fall short in predictive power compared to their more complex counterparts. To bridge the gap between intelligibility and performance, we propose an adaptation of tabular transformer networks designed to identify marginal feature effects. We provide theoretical justifications that marginal feature effects can be accurately identified, and our ablation study demonstrates that the proposed model efficiently detects these effects, even amidst complex feature interactions. To demonstrate the model's predictive capabilities, we compare it to several interpretable as well as black-box models and find that it can match black-box performances while maintaining intelligibility. The source code is vailable at https://anonymous.4open.science/r/nmfrmr-B086.
Primary Area: interpretability and explainable AI
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Submission Number: 1317
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