A implementation of Random Forest with pure Common Lisp. It also includes a implementation of Global Refinement of Random Forest (Ren, Cao, Wei and Sun. "Global Refinement of Random Forest" CVPR2015).
Features and Limitations
- Faster and more accurate than other major implementations such as scikit-learn (Python/Cython) or ranger (R/C++)
|MNIST||96.95%, 41.72sec||97.17%, 69.34sec||98.29%, 12.68sec|
|letter||96.38%, 2.569sec||96.42%, 1.828sec||97.32%, 3.497sec|
|covtype||94.89%, 263.7sec||83.95%, 139.0sec||96.01%, 103.9sec|
|usps||93,47%, 3.583sec||93.57%, 11.70sec||94.96%, 0.686sec|
Supporting parallelization of training and prediction (SBCL Only)
It also includes Global Pruning algorithm of Random Forest which can make the model extremely compact
Currently, regression is not implemented and only classification is available
In quicklisp's local-projects directory,
git clone https://github.com/masatoi/cl-random-forest.git
When using Roswell,
ros install masatoi/cl-random-forest
See examples. The most basic example is in example/simple.lisp.
Satoshi Imai (email@example.com)
This software is released under the MIT License, see LICENSE.txt.
- Satoshi Imai, NIL
- MIT Licence