DTSemNet gives an exact, invertible neural-network encoding of hard oblique decision trees that supports direct gradient training for both classification and regression without probabilistic softening or quantized estimators.
arXiv preprint arXiv:1806.06988 , year=
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abstract
Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. We evaluate DNDT on several tabular datasets, verify its efficacy, and investigate similarities and differences between DNDT and vanilla decision trees. Interestingly, DNDT self-prunes at both split and feature-level.
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TabTransformer uses Transformer self-attention to generate contextual embeddings from categorical features in tabular data, outperforming prior deep learning methods by at least 1% mean AUC and matching tree-based ensembles on 15 public datasets while showing robustness to missing and noisy features
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Approximation-Free Differentiable Oblique Decision Trees
DTSemNet gives an exact, invertible neural-network encoding of hard oblique decision trees that supports direct gradient training for both classification and regression without probabilistic softening or quantized estimators.
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TabTransformer: Tabular Data Modeling Using Contextual Embeddings
TabTransformer uses Transformer self-attention to generate contextual embeddings from categorical features in tabular data, outperforming prior deep learning methods by at least 1% mean AUC and matching tree-based ensembles on 15 public datasets while showing robustness to missing and noisy features