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scikit-optimize/scikit-optimize

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 25 Mar 2018Publisher:Zenodo
Authors: Head, Tim; MechCoder; Louppe, Gilles; 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

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.

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Keywords

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

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download
selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
107
Top 1%
Top 10%
Top 10%
544
12