# kjw0612/awesome-random-forest

Random Forest - a curated list of resources regarding random forest

Repository: https://github.com/kjw0612/awesome-random-forest
Canonical: https://ross.abutalabs.com/products/awesome-random-forest
Homepage: http://jiwonkim.org/awesome-random-forest
License Family: other
Last push: 2023-11-16T18:51:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4097, "days_push": 1021, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1254, forks 333 (observed 2026-08-28T04:04:08.697249+00:00)

## What it is
A curated list of resources on random forests and tree-based methods, including code libraries, lectures, books, papers, and applications. It covers implementations across languages like Python, R, C/C++, and Go, plus computer vision applications.

## Use cases
- find random forest implementations in different languages
- learn the theory behind decision forests and bagging
- find papers on tree-based model variants
- discover random forest applications in computer vision
- compare random forest libraries like ranger and scikit-learn
- find tutorials and lectures on ensemble methods

## When to choose
- you want a starting point for learning random forests
- you need to survey available implementations across languages
- you're researching tree-based methods for computer vision

## When to avoid
- you need actively maintained or up-to-date resources
- you want a runnable tool rather than a reference list
- you need resources on deep learning instead of tree ensembles

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, developer-tools
- domain: machine-learning, tutorials, computer-vision, awesome-lists
- platform: cross-platform
- tags: awesome-list, random-forest, ensemble-methods, curated-resources, decision-trees, bagging, boosting

## Member repositories
- kjw0612/awesome-random-forest (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:08.697249+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 2026-08-30T05:07:24.593210+00:00, confidence not recorded.
  - readme: https://github.com/kjw0612/awesome-random-forest (fetched 2026-08-28T04:04:08.697249+00:00, sha a6243b94d347)
- Data as of 2026-08-30T08:39:29.467469+00:00.