Metadata-Version: 2.1
Name: wildwood
Version: 0.1
Summary: scikit-learn compatible alternative random forests algorithms
Home-page: https://wildwood.readthedocs.io
Keywords: python,machine-learning,classification,regression,random-forests,robust-methods
Author: Stéphane Gaïffas
Author-email: stephane.gaiffas@gmail.com
Requires-Python: >=3.6
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: numba (>=0.48)
Requires-Dist: numpy (>=1.17)
Requires-Dist: scikit-learn (>=0.22)
Requires-Dist: scipy (>=1.3.2)
Requires-Dist: tqdm (>=4.36)
Project-URL: Documentation, https://wildwood.readthedocs.io
Project-URL: Repository, https://github.com/pyensemble/wildwood
Description-Content-Type: text/markdown

# forest
Advanced random forest methods in Python

