Data Pipelines

cl-random-forest

Random Forest implementation in Common Lisp for machine learning tasks.

Status
Generally available
Last checked
Checked · Oct 6, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

cl-random-forest is an open-source library that provides a Random Forest algorithm implemented in Common Lisp. It's useful for developers and data scientists who prefer or require the use of Common Lisp for their machine learning projects.

cl-random-forest stands out as a dedicated Random Forest implementation in Common Lisp, offering developers and researchers an open-source solution within the language ecosystem.

When it fits — and when it doesn’t

A good fit for

  • Common Lisp developers who need a robust implementation of Random Forest for their projects.

  • Educators and students learning about ensemble methods in machine learning using Common Lisp.

Look elsewhere if

  • Projects requiring high performance or scalability beyond what can be achieved with Common Lisp.

  • Developers preferring languages other than Common Lisp for machine learning tasks.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited community and support

    Common Lisp is not as widely used as languages like Python or R, leading to a smaller user base and fewer resources for troubleshooting.

  2. 02

    Performance limitations compared to optimized implementations in other languages

    While Common Lisp can be efficient, the performance of cl-random-forest might not match highly optimized libraries written in C or specialized machine learning frameworks like scikit-learn.

  3. 03

    Limited integrations with popular data science tools and platforms

    Common Lisp has fewer ecosystem integrations compared to languages such as Python, which has extensive support for various data processing and visualization libraries.

What it does well

  • Implementation of the Random Forest algorithm in Common Lisp.

  • Suitable for classification and regression tasks.

  • Open-source with MIT license.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up cl-random-forest

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →