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The LogitBoost algorithm

Project description

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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: https://logitboost.readthedocs.io

Installation

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

git clone https://github.com/artemmavrin/logitboost.git
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.

References

[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.

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