Generalizing Tree Models for Improving Prediction Accuracy

Jaemin Yoo, Lee Sael

Abstract: Can we generalize and improve the representation power of tree models? Tree models are often favored over deep neural networks due to their interpretable structures in problems where the interpretability is required, such as in the classification of feature-based data where each feature is meaningful. However, most tree models have low accuracies and easily overfit to training data. In this work, we propose Decision Transformer Network (DTN), our highly accurate and interpretable tree model based on our generalized framework of tree models, decision transformers. Decision transformers allow us to describe tree models in the context of deep learning. Our DTN is proposed based on improving the generalizable components of the decision transformer, which increases the representation power of tree models while preserving the inherent interpretability of the tree structure. Our extensive experiments on 121 feature-based datasets show that DTN outperforms the state-of-the-art tree models and even deep neural networks.

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