This Common Lisp repository collects algorithms for online linear classification.
Open source
24 alternativescl-random-forest
This repository implements random forest models in Common Lisp for classification tasks.
About cl-random-forest
cl-random-forest is an open-source Common Lisp repository that implements random forest models for multiclass classification and univariate regression. It includes an implementation of Global Refinement of Random Forest from a 2015 CVPR paper, which the repository states delivers faster and more accurate performance than scikit-learn and ranger on benchmark datasets. The library supports parallel training and prediction on SBCL and CCL, includes a global pruning algorithm for creating compact models, and can be installed via Quicklisp or Roswell. Multivariate regression is not currently implemented.
Key features
- Implements random forest for multiclass classification and univariate regression
- Includes Global Refinement of Random Forest (CVPR 2015)
- Supports parallel training and prediction on SBCL and CCL
- Includes global pruning algorithm for compact models
- Supports installation via Quicklisp and Roswell
- Provides functions to build, predict, and test decision trees and forests
Best for: Common Lisp developers building multiclass classification or univariate regression models with random forest algorithms.
Good for
- Faster and more accurate than scikit-learn and ranger on benchmark datasets
- Supports parallel training and prediction on tested Common Lisp implementations
- Includes global pruning algorithm to produce extremely compact models
Watch out for
- Multivariate regression is not implemented
- Two utility packages require qualified symbol access and separate loading
cl-random-forest at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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cl-random-forest FAQ
What machine learning tasks does cl-random-forest support?
It supports multiclass classification and univariate regression; multivariate regression is not implemented.
How can cl-random-forest be installed?
It can be installed via Quicklisp by cloning the repository to Quicklisp's local-projects directory, or via Roswell with the ros install command.
Does cl-random-forest support parallelization?
Yes, parallel training and prediction are supported and tested on SBCL and CCL.
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