Hyperparameter optimization: Difference between revisions

Revision as of 11:19, 28 March 2018

Revision as of 03:16, 11 April 2018

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* [http://www.cs.ubc.ca/labs/beta/Projects/autoweka/ Auto-WEKA]<ref name="autoweka">{{cite journal | vauthors = Kotthoff L, Thornton C, Hoos HH, Hutter F, Leyton-Brown K | year = 2017 | title = Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA | url = http://jmlr.org/papers/v18/16-261.html | journal = Journal of Machine Learning Research | pages = 1–5 }}</ref> is a Bayesian hyperparameter optimization layer on top of [[Weka (machine learning)|WEKA]].

* [https://github.com/automl/auto-sklearn Auto-sklearn]<ref name="autosklearn">{{cite journal | vauthors = Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F | year = 2015 | title = Efficient and Robust Automated Machine Learning | url = https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning | journal = Advances in Neural Information Processing Systems 28 (NIPS 2015) | pages = 2962–2970 }}</ref> is a Bayesian hyperparameter optimization layer on top of [[scikit-learn]].

* [https://github.com/mlr-org/mlrMBO mlrMBO], also with [https://github.com/mlr-org/mlr mlr], is an [[R (programming language)|R]] package for model-based/Bayesian optimization of black-box functions.

* [https://github.com/PhilippPro/tuneRanger tuneRanger] is an R package for tuning random forests using model-based optimization.

===Gradient based===

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* [https://github.com/coin-or/rbfopt rbfopt] is a Python package that uses a [[radial basis function]] model<ref name=abs1705.08520/>

* [https://github.com/callowbird/Harmonica Harmonica] is a Python package for spectral hyperparameter optimization.<ref name=abs1706.00764/>

===Multiple===

* [https://github.com/mlr-org/mlr mlr] is an [[R (programming language)|R]] package that contains a large number of different hyperparameter optimization techniques.

== See also ==