#artificial intelligence
#machine learning
#natural language processing
#data science
Preprint
Open access
Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations
Kyaw Hpone Myint
Zhe Wu
Alexandre G. R. Day
Giri Iyengar
Oct 2026
Artificial Intelligence
Machine Learning
Natural Language Processing
Data Science
Abstract
Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an efficient, scalable method for generating synthetic pre-training data to enable meta-learning of decision trees. Our approach samples near-optimal decision trees synthetically, creating large-scale, realistic datasets. Using the MetaTree transformer architecture, we demonstrate that this method achieves performance comparable to pre-training on real-world data or with computationally expensive optimal decision trees. This strategy significantly reduces computational costs, enhances data generation flexibility, and paves the way for scalable and efficient meta-learning of interpretable decision tree models.
