tune-sklearn:Scikit-Learn的GridSearchCV RandomizedSearchCV的直接替代品-但具有最先进的超参数调整技术

上传者: 42143161 | 上传时间: 2022-04-22 17:43:10 | 文件大小: 72KB | 文件类型: ZIP
调谐斯克莱恩 Tune-sklearn是Scikit-Learn的模型选择模块(GridSearchCV,RandomizedSearchCV)的替代品,它具有尖端的超参数调整技术。 产品特点 以下是tune-sklearn提供的功能: 与Scikit-Learn API的一致性:在标准Scikit-Learn脚本中更改少于5行即可使用API​​ []。 现代调整技术:tune-sklearn使您可以通过简单地切换几个参数来轻松利用贝叶斯优化,HyperBand,BOHB和其他优化技术。 框架支持:tune-sklearn主要用于调整Scikit-Learn模型,但它也支持并提供了许多其他带有Scikit-Learn包装器的框架的示例,例如Skorch(Pytorch)[ ],KerasClassifier(Keras)[ ],和XGBoostClassifier(XGBoost)[]。 向上扩展:Tune-sklearn利用 (一个用于分布式超参数调整的库)在不更改代码的情况下并行化多个核甚至多个机器上的交叉验证。 查看我们的和(针对master分支)。 安装 依存关

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