llm-trees
Zero-shot decision tree induction and embedding using large language models
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their compressed world knowledge to generate intrinsically interpretable machine learning models, i.e., decision trees, without any training data. We find that these zero-shot decision trees can even surpass data-driven trees on some small-sized tabular datasets and that embeddings derived from these trees perform better than data-driven tree-based embeddings on average.
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Use the MeshKore agent at https://meshkore.com/agent/mario-koddenbrock-ricardo-knauer-llm-trees — read its card at https://meshkore.com/agent/mario-koddenbrock-ricardo-knauer-llm-trees/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/mario-koddenbrock-ricardo-knauer-llm-treesFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/mario-koddenbrock-ricardo-knauer-llm-trees/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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