Skip to main content

The LogitBoost algorithm

Project description

Python Version PyPI Package Version Travis CI Build Status Code Coverage Documentation Status GitHub License

This is a Python implementation of the LogitBoost classification algorithm [1] built on top of scikit-learn. It supports both binary and multiclass classification; see the examples.

This package provides a single class, LogitBoost, which can be used out-of-the-box like any sciki-learn estimator.

Documentation website:


The latest version of LogitBoost can be installed directly after cloning from GitHub.

git clone
cd logitboost
make install

Moreover, LogitBoost is on the Python Package Index (PyPI), so a recent version of it can be installed with the pip tool.

python -m pip install logitboost

This project was developed in Python 3.7, and it is tested to also work with Python 3.6.


[1]Jerome Friedman, Trevor Hastie, and Robert Tibshirani. “Additive Logistic Regression: A Statistical View of Boosting”. The Annals of Statistics. Volume 28, Number 2 (2000), pp. 337–374. JSTOR. Project Euclid.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for logitboost, version 0.7
Filename, size File type Python version Upload date Hashes
Filename, size logitboost-0.7-py3-none-any.whl (9.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size logitboost-0.7.tar.gz (9.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page