MotherTree: Meta-learning on synthetic data improves decision tree training
MotherTree is a tabular transformer designed to automatically create hard, axis-aligned decision trees in one forward pass. It meta-learns how to build these trees by pre-training on synthetic data using different priors. This method significantly improves decision tree training performance compared to traditional methods, showing that meta-learning initialization helps task-specific tuning.