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 API conventions. Computationally demanding parts are implemented using Numba.
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.
To install ivalice from pip, type:
pip install https://github.com/mblondel/ivalice/archive/master.zipTo install ivalice from source, type:
git clone https://github.com/mblondel/ivalice.git
cd ivalice
sudo python setup.py installhttps://github.com/mblondel/ivalice
Mathieu Blondel, 2014-present