Software
We've compiled a list of 7 free and paid alternatives to python auto-sklearn. The primary competitors include H2O.ai, R Caret. In addition to these, users also draw comparisons between python auto-sklearn and R mlr, ML.NET, R MLstudio. Also you can look at other similar options here: About.
We've compiled a list of 7 free and paid alternatives to python auto-sklearn. The primary competitors include H2O.ai, R Caret. In addition to these, users also draw comparisons between python auto-sklearn and R mlr, ML.NET, R MLstudio. Also you can look at other similar options here: About.
H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive...
The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions...
The ML Studio is interactive for EDA, statistical modeling and machine learning applications.
auto-sklearn is an automated machine learning toolkit.
auto-sklearn is an automated machine learning toolkit.
Linux
Mac
Windows
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Learn more about the technology behind auto-sklearn by reading our paper published at NIPS 2015 .
hat))
This will run for one hour and should result in an accuracy above 0.98.
License
auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license.
Citing auto-sklearn
If you use auto-sklearn in a scientific publication, we would appreciate a reference to the following paper:
Efficient and Robust Automated Machine Learning, Feurer et al., Advances in Neural Information Processing Systems 28 (NIPS 2015).
Bibtex entry:
@incollection{NIPS2015_5872,
title = {Efficient and Robust Automated Machine Learning},
author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and
Springenberg, Jost and Blum, Manuel and Hutter, Frank},
booktitle = {Advances in Neural Information Processing Systems 28},
editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},
pages = {2962--2970},
year = {2015},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}
Contributing
We appreciate all contribution to auto-sklearn, from bug reports and documentation to new features. If you want to contribute to the code, you can pick an issue from the issue tracker which is marked with Needs contributer.
Machine Learning
Share your opinion about the software, leave a review and help make it even better!
Suggest Changes
Your Feedback
Your vote has been counted.
Do you have experience using this software?
