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A Python implementation of Reinforcement Learning Trees (Zhu et al., 2015). RLT leverages "look-ahead" reinforcement learning to master high-dimensional, sparse data where standard Random Forests fail. Includes reproduction of synthetic scenarios, UCI benchmarks vs. XGBoost, explainability analysis.
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60
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8
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