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scikit-optimize/scikit-optimize

Name: scikit-optimize/scikit-optimize
Keywords: python, bayesopt, machine-learning, hyperparameter, scientific-computing, bayesian-optimization, optimization

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 25 Mar 2018Publisher:Zenodo

Authors: Tim Head; MechCoder; Gilles Louppe; Iaroslav Shcherbatyi; fcharras; Zé Vinícius; cmmalone; +23 Authors

doi: 10.5281/zenodo.6451894 , 10.5281/zenodo.5565057 , 10.5281/zenodo.1157319 , 10.5281/zenodo.4014775 , 10.5281/zenodo.1207017 , 10.5281/zenodo.6451893

scikit-optimize/scikit-optimize

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Abstract

Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.

Related Organizations

King’s University
United States
University of Paris-Saclay
France
Technical University of Munich
Germany
Polytechnic University of Milan
Italy
University of California, Berkeley
United States

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Keywords

python, bayesopt, machine-learning, hyperparameter, scientific-computing, bayesian-optimization, optimization

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5 Research products, page 1 of 1

SAMBO: Sequential And Model-Based Optimization: Efficient global optimization in Python
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SAMBO: Sequential And Model-Based Optimization: Efficient global optimization in Python
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scikit-optimize/scikit-optimize: v0.5rc1
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scikit-optimize/scikit-optimize: High five - v0.5
2018HasVersion
scikit-optimize/scikit-optimize: v0.5.1 - re-release
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Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	114
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%