<ul><li>Deep neural networks have been powerful in predictive tasks across various fields, including tabular data problems.</li><li>The transformer architecture has challenged gradient-based decision trees in handling tabular data.</li><li>However, the black-box nature of deep tabular transformer networks makes it difficult to interpret marginal feature effects.</li><li>A proposed adaptation of tabular transformer networks aims to identify and maintain intelligible marginal feature effects while maintaining predictive power.</li></ul>

Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks

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