Machine Learning with Scikit-Learn and Tensorflow 7.4 Random Patches和Random Subspaces
2017-04-06 10:05
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书籍信息
Hands-On Machine Learning with Scikit-Learn and Tensorflow
出版社: O’Reilly Media, Inc, USA
平装: 566页
语种: 英语
ISBN: 1491962291
条形码: 9781491962299
商品尺寸: 18 x 2.9 x 23.3 cm
ASIN: 1491962291
系列博文为书籍中文翻译
代码以及数据下载:https://github.com/ageron/handson-ml
bagging同时支持针对特征的抽样,通过参数max_features和bootstrap_features实现,其含义与max_samples和bootstrap类似。此时,所有模型都使用部分特征进行训练。
当处理高维数据(例如图像)时,这样的方法是特别有用的。同时针对训练数据和特征进行抽样是Random Patches,只针对特征抽样而不针对训练数据抽样(bootstrap=False,max_samples=1.0)是Random Subspaces。
针对特征的抽样能够提升模型多样性,从而增加偏差,减少方差。
Hands-On Machine Learning with Scikit-Learn and Tensorflow
出版社: O’Reilly Media, Inc, USA
平装: 566页
语种: 英语
ISBN: 1491962291
条形码: 9781491962299
商品尺寸: 18 x 2.9 x 23.3 cm
ASIN: 1491962291
系列博文为书籍中文翻译
代码以及数据下载:https://github.com/ageron/handson-ml
bagging同时支持针对特征的抽样,通过参数max_features和bootstrap_features实现,其含义与max_samples和bootstrap类似。此时,所有模型都使用部分特征进行训练。
当处理高维数据(例如图像)时,这样的方法是特别有用的。同时针对训练数据和特征进行抽样是Random Patches,只针对特征抽样而不针对训练数据抽样(bootstrap=False,max_samples=1.0)是Random Subspaces。
针对特征的抽样能够提升模型多样性,从而增加偏差,减少方差。
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