Machine Learning with Scikit-Learn and Tensorflow 6 决策树(章节目录)
2017-04-01 22:41
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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
与SVM类似,决策树是强大的机器学习算法,能够用于回归和分类。决策树可以拟合复杂的数据,例如第2章训练的模型完美拟合训练数据(实际上过拟合)。
决策树是随机森林的基本组成成分,随机森林是目前最为强大的机器学习算法之一。
在本章,首先,我们讨论决策树的训练、可视化以及如何利用决策树进行预测。然后,我们介绍scikit-learn使用的CART算法,并且讨论如何限制决策树的训练过程以及如何使用决策树进行回归。最后,我们讨论决策树的局限性。
6.1 决策树的训练与可视化
http://blog.csdn.net/qinhanmin2010/article/details/68499196
6.2 进行预测
http://blog.csdn.net/qinhanmin2010/article/details/68558969
6.3 预测类别概率
http://blog.csdn.net/qinhanmin2010/article/details/68584717
6.4 CART算法
http://blog.csdn.net/qinhanmin2010/article/details/68935565
6.5 计算复杂度
http://blog.csdn.net/qinhanmin2010/article/details/68936099
6.6 基尼不纯度/熵
http://blog.csdn.net/qinhanmin2010/article/details/68937241
6.7 规范化超参数
http://blog.csdn.net/qinhanmin2010/article/details/68940762
6.8 决策树回归
http://blog.csdn.net/qinhanmin2010/article/details/68941236
6.9 决策树局限性
http://blog.csdn.net/qinhanmin2010/article/details/68942741
6.10 练习
http://blog.csdn.net/qinhanmin2010/article/details/68944735
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
与SVM类似,决策树是强大的机器学习算法,能够用于回归和分类。决策树可以拟合复杂的数据,例如第2章训练的模型完美拟合训练数据(实际上过拟合)。
决策树是随机森林的基本组成成分,随机森林是目前最为强大的机器学习算法之一。
在本章,首先,我们讨论决策树的训练、可视化以及如何利用决策树进行预测。然后,我们介绍scikit-learn使用的CART算法,并且讨论如何限制决策树的训练过程以及如何使用决策树进行回归。最后,我们讨论决策树的局限性。
6.1 决策树的训练与可视化
http://blog.csdn.net/qinhanmin2010/article/details/68499196
6.2 进行预测
http://blog.csdn.net/qinhanmin2010/article/details/68558969
6.3 预测类别概率
http://blog.csdn.net/qinhanmin2010/article/details/68584717
6.4 CART算法
http://blog.csdn.net/qinhanmin2010/article/details/68935565
6.5 计算复杂度
http://blog.csdn.net/qinhanmin2010/article/details/68936099
6.6 基尼不纯度/熵
http://blog.csdn.net/qinhanmin2010/article/details/68937241
6.7 规范化超参数
http://blog.csdn.net/qinhanmin2010/article/details/68940762
6.8 决策树回归
http://blog.csdn.net/qinhanmin2010/article/details/68941236
6.9 决策树局限性
http://blog.csdn.net/qinhanmin2010/article/details/68942741
6.10 练习
http://blog.csdn.net/qinhanmin2010/article/details/68944735
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