# kingfengji/gcForest

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Repository: https://github.com/kingfengji/gcForest
Canonical: https://ross.abutalabs.com/products/gcforest
Homepage: http://lamda.nju.edu.cn/code_gcForest.ashx
Language: Python
License Family: other
Topics: machine-learning, random-forest, ensemble-learning, deep-forest
Last push: 2021-02-04T05:10:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3380, "days_push": 2036, "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 1312, forks 426 (observed 2026-08-28T04:04:20.231103+00:00)

## What it is
Official Python implementation of gcForest, the multi-grained cascade deep forest algorithm proposed in the IJCAI-2017 paper 'Deep Forest: Towards an Alternative to Deep Neural Networks'. It offers a scikit-learn style API and is now superseded by the maintained deep-forest package.

## Use cases
- train deep forest models as an alternative to neural networks
- run the gcForest algorithm from the IJCAI 2017 paper
- fit ensemble tree models with a scikit-learn style API
- experiment with cascade forest classifiers on my dataset
- reproduce results from the deep forest paper

## When to choose
- you need the exact original gcForest implementation for research reproduction
- you want a tree-based alternative to deep neural networks on tabular data
- you prefer a scikit-learn style fit/predict API for ensemble forests

## When to avoid
- you need a maintained library - use LAMDA-NJU/Deep-Forest (pip install deep-forest) instead
- you need Python 3.8+ support since this targets Python 2.7/3.5
- you need a permissive license for commercial use - it is academic-use only with no license file

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning
- domain: machine-learning
- platform: python
- tags: deep-forest, gcforest, ensemble-learning, random-forest, research-code, python2, algorithms

## Member repositories
- kingfengji/gcForest (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.231103+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 2026-08-30T04:49:05.442205+00:00, confidence not recorded.
  - readme: https://github.com/kingfengji/gcForest (fetched 2026-08-28T04:04:20.231103+00:00, sha 4883e1a91553)
- Data as of 2026-08-30T08:39:29.467469+00:00.