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Data Science Archive@DataScienceArchive P.30
DATASCIENCEARCHIVE Telegram 30
一个 ML 扩展包,配合scikit-learn 一起食用还是很不错的,以前用过,主要优势在于 ensemble 和各种常用应用层面的封装,毕竟scikit-learn 里面不常用的方法还是有点多。
link: http://rasbt.github.io/mlxtend/
作者是威斯康辛麦迪逊的统计系老师,也是这本《Python Machine Learning》的作者。
书:https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130



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一个 ML 扩展包,配合scikit-learn 一起食用还是很不错的,以前用过,主要优势在于 ensemble 和各种常用应用层面的封装,毕竟scikit-learn 里面不常用的方法还是有点多。
link: http://rasbt.github.io/mlxtend/
作者是威斯康辛麦迪逊的统计系老师,也是这本《Python Machine Learning》的作者。
书:https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130

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