Content deleted Content added
Line 112:
* [https://github.com/hyperopt/hyperopt hyperopt] and [https://github.com/hyperopt/hyperopt-sklearn hyperopt-sklearn] are Python packages which include random search.
* [[scikit-learn]] is a Python package which includes [http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html random] search.
* [http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html H2O AutoML] provides automated data preparation, hyperparameter tuning via random search, and stacked ensembles in a distributed machine learning platform.
===Bayesian===
Line 127 ⟶ 128:
* [https://github.com/yelp/MOE MOE] MOE is a Python/C++/CUDA library implementing Bayesian Global Optimization using Gaussian Processes.
* [http://www.cs.ubc.ca/labs/beta/Projects/autoweka/ Auto-WEKA] is a Bayesian hyperparameter optimization layer on top of [[Weka (machine learning)|WEKA]].
* [https://github.com/automl/auto-sklearn Auto-sklearn] is a Bayesian hyperparameter optimization layer on top of [[scikit-learn]].
===Gradient based===
* [https://github.com/HIPS/hypergrad hypergrad] is a Python package for differentiation with respect to hyperparameters.<ref name=abs1502.03492/>
===Evolutionary===
* [https://github.com/rhiever/tpot TPOT]<ref>{{cite journal | vauthors = Olson RS, Urbanowicz RJ, Andrews PC, Lavender NA, Kidd L, Moore JH | year = 2016 | title = Automating biomedical data science through tree-based pipeline optimization | url = https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9 | journal = Proceedings of EvoStar 2016 | pages = 123-137 | doi = 10.1007/978-3-319-31204-0_9 }}</ref><ref>{{cite journal | vauthors = Olson RS, Bartley N, Urbanowicz RJ, Moore JH | year = 2016 | title = Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science | url = https://dl.acm.org/citation.cfm?id=2908918 | journal = Proceedings of EvoBIO 2016 | pages = 485-492 | doi = 10.1145/2908812.2908918 }}</ref> is a Python package that automatically creates and optimizes full machine learning pipelines using [[genetic programming]].
* [https://github.com/
===Other===
Line 157 ⟶ 163:
===Multiple===
* [https://github.com/mlr-org/mlr mlr] is a [[R]] package that contains a large number of different hyperparameter optimization techniques for machine learning problems.
▲* [https://github.com/rhiever/tpot TPOT] is a Python library that automatically creates and optimizes full machine learning pipelines using genetic programming.
== See also ==
| |||