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Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Open accessAbstract
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization.This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train.The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python.This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization.The paper closes with some discussion of ongoing and future work.
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Cites
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
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Metadata
- Authors
- Bergstra, J., Yamins, D., & Cox, D.
- Venue
- Proceedings of the Python in Science Conference
- Publisher
- SciPy
- Published
- 2013
- Pages
- 13-19
- ISSN
- 2575-9752 (ISSN-L)
- OpenAlex
- W2189149359
- Cited by
- 781
- References
- 1
- FWCI
- 6.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Bergstra, J., Yamins, D., & Cox, D. (2013). Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. Proceedings of the Python in Science Conference. https://doi.org/10.25080/majora-8b375195-003
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