Skip to content

rovedream/auto-sklearn

 
 

Repository files navigation

auto-sklearn

auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

Find the documentation here

Automated Machine Learning in four lines of code

import autosklearn.classification
cls = autosklearn.classification.AutoSklearnClassifier()
cls.fit(X_train, y_train)
predictions = cls.predict(X_test)

Relevant publications

Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter
Advances in Neural Information Processing Systems 28 (2015)
http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf

Auto-Sklearn 2.0: The Next Generation
Authors: Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter
arXiv:2007.04074 [cs.LG], 2020 https://arxiv.org/abs/2007.04074

Status

Status for master branch

Build Status Code Health codecov

Status for development branch

Build Status Code Health codecov

About

Automated Machine Learning with scikit-learn

Resources

License

BSD-3-Clause, Unknown licenses found

Licenses found

BSD-3-Clause
LICENSE.txt
Unknown
COPYING

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 99.1%
  • Other 0.9%