ivalice
ivalice copied to clipboard
Boosting and ensemble learning in Python.
.. -- mode: rst --
ivalice
Boosting and ensemble learning library in Python.
Algorithms supported:
- Classification and regression trees (work in progress)
- Random forests (work in progress)
- Gradient Boosting
- McRank
- LambdaMART
ivalice follows the scikit-learn <http://scikit-learn.org>
_ API conventions.
Computationally demanding parts are implemented using Numba <http://numba.pydata.org>
_.
Dependencies
ivalice needs Python >= 2.7, setuptools, Numpy >= 1.3, SciPy >= 0.7, scikit-learn >= 0.15.1 and Numba >= 0.13.4.
To run the tests you will also need nose >= 0.10.
Installation
To install ivalice from pip, type::
pip install https://github.com/mblondel/ivalice/archive/master.zip
To install ivalice from source, type::
git clone https://github.com/mblondel/ivalice.git cd ivalice sudo python setup.py install
On Github
https://github.com/mblondel/ivalice
Author
Mathieu Blondel, 2014-present