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GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
March 13, 2024, 4:43 a.m. | Sascha Marton, Stefan L\"udtke, Christian Bartelt, Heiner Stuckenschmidt
cs.LG updates on arXiv.org arxiv.org
Abstract: Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose $\text{GRANDE}$, $\text{GRA}$die$\text{N}$t-Based $\text{D}$ecision Tree $\text{E}$nsembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords …
arxiv cs.lg data decision gradient grande tabular tabular data tree type
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