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Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations
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 sy
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