Awesome Decision Tree Papers
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
What is Awesome Decision Tree Papers?
Awesome Decision Tree Papers is a curated repository of academic research papers on decision trees, classification trees, and regression trees, often including links to implementations. It aggregates publications from top-tier conferences, providing a centralized resource for exploring advanced tree-based machine learning techniques and their applications.
Machine learning researchers, data scientists, and graduate students who need a comprehensive reference for state-of-the-art tree-based methods and want to implement or extend these algorithms.
It saves significant time in literature review by collecting relevant papers in one place, includes code links for practical use, and covers a wide range of specialized topics from fairness to efficiency, making it a go-to resource for both theoretical and applied work.
Overview
A collection of research papers on decision, classification and regression trees with implementations.
Use Cases
Best For
- Researchers conducting literature reviews on decision tree advancements
- Practitioners seeking implemented tree-based models for specific tasks
- Students looking for foundational and cutting-edge papers on tree methods
- Developers needing code references for tree algorithms like XGBoost or LightGBM
- Teams exploring interpretable and fair machine learning models
- Academics preparing lectures or courses on tree-based learning
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