Escaping the Forest: Sparse Interpretable Neural Networks for Tabular Data

• URL: http://arxiv.org/abs/2410.17758v1
• Date: Wed, 23 Oct 2024 10:50:07 GMT
• Title: Escaping the Forest: Sparse Interpretable Neural Networks for Tabular Data
• Authors: Salvatore Raieli, Abdulrahman Altahhan, Nathalie Jeanray, Stéphane Gerart, Sebastien Vachenc,
• Abstract summary: We show that our models, Sparse TABular NET or sTAB-Net with attention mechanisms, are more effective than tree-based models. They achieve better performance than post-hoc methods like SHAP.
• Score: 0.0
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• Abstract: Tabular datasets are widely used in scientific disciplines such as biology. While these disciplines have already adopted AI methods to enhance their findings and analysis, they mainly use tree-based methods due to their interpretability. At the same time, artificial neural networks have been shown to offer superior flexibility and depth for rich and complex non-tabular problems, but they are falling behind tree-based models for tabular data in terms of performance and interpretability. Although sparsity has been shown to improve the interpretability and performance of ANN models for complex non-tabular datasets, enforcing sparsity structurally and formatively for tabular data before training the model, remains an open question. To address this question, we establish a method that infuses sparsity in neural networks by utilising attention mechanisms to capture the features' importance in tabular datasets. We show that our models, Sparse TABular NET or sTAB-Net with attention mechanisms, are more effective than tree-based models, reaching the state-of-the-art on biological datasets. They further permit the extraction of insights from these datasets and achieve better performance than post-hoc methods like SHAP.

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