# benedekrozemberczki/awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

Repository: https://github.com/benedekrozemberczki/awesome-decision-tree-papers
Canonical: https://ross.abutalabs.com/products/awesome-decision-tree-papers
Language: Python
License: CC0-1.0
License Family: permissive
Topics: decision-tree, decision-tree-classifier, gradient-boosting, gradient-boosting-machine, regression-tree, cart, machine-learning, machine-learning-research, random-forest, xgboost, lightgbm, catboost, classification-trees, decision-tree-model, decision-tree-learning, classification-model, classifier, tree-ensemble, ensemble-learning, statistical-learning
Last push: 2025-12-28T23:17:27+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 59, release rhythm 8, longevity 100
- inputs: {"age_days": 2671, "days_push": 248, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2473, forks 342 (observed 2026-08-28T04:06:54.809109+00:00)

## What it is
A curated awesome-list of research papers on decision, classification, and regression trees, including links to implementations. It covers papers from major ML, CV, NLP, data mining, and AI conferences.

## Use cases
- find research papers on decision trees
- learn about random forests and tree ensembles
- find gradient boosting paper implementations
- survey classification and regression tree literature
- find CART and XGBoost research with code
- prepare a literature review on tree-based models

## When to choose
- you need a curated reading list on tree-based machine learning
- you want papers paired with open-source implementations
- you are researching decision tree learning or ensemble methods

## When to avoid
- you need a working decision tree library rather than papers
- you want tutorials or courses instead of academic literature

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation
- domain: machine-learning, tutorials, awesome-lists
- platform: python
- tags: decision-trees, random-forest, gradient-boosting, research-papers, curated-list, tree-ensembles

## Member repositories
- benedekrozemberczki/awesome-decision-tree-papers (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:54.809109+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:28:31.602268+00:00, confidence not recorded.
  - readme: https://github.com/benedekrozemberczki/awesome-decision-tree-papers (fetched 2026-08-28T04:06:54.809109+00:00, sha a6d2fbcdecf1)
- Data as of 2026-08-30T08:39:29.467469+00:00.