Learning a Decision Tree Algorithm with Transformers

Decision trees are renowned for their interpretability capability to achieve high predictive performance, especially on tabular data. Traditionally, they are constructed through recursive algorithms, where they partition the data at every node in a tree. However, identifying the best partition is challenging, as decision trees optimized for local segments may not bring global generalization. To address this, we introduce MetaTree, which trains a transformer-based model on filtered outputs from classical algorithms to produce strong decision trees for classification. Specifically, we …

algorithm algorithms capability cs.ai cs.cl cs.lg data decision decision trees every global interpretability node performance predictive recursive tabular tabular data through transformers tree trees