Machine Learning with Scikit-Learn and Tensorflow 7 集成学习和随机森林(章节目录)
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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
设想我们随机询问成千上万的人特定复杂的问题,将他们的答案进行整合。很多时候,我们得到的结果甚至优于领域的专家,这就是群体的智慧。类似地,如果我们整合诸多模型的结果,我们通常能够得到更好的结果,这就是集成学习的基本思想。
例如,我们可以在训练数据的不同子集上训练决策树,进行预测时,我们将多数模型的结果作为最终的预测结果,这就是随机森林的基本思想,随机森林是目前最为强大的机器学习算法之一。
此外,在机器学习项目的结束阶段,当我们获得若干优秀的模型后,我们需要对这些模型进行整合,创造更加优秀的模型。事实上,许多数据竞赛的优胜方法包含集成学习的思想。
在本章,我们讨论流行的集成学习方法,包括bagging,boosting,stacking。我们也会讨论随机森林。
7.1 Voting Classifier
http://blog.csdn.net/qinhanmin2010/article/details/69350639
7.2 Bagging和Pasting
http://blog.csdn.net/qinhanmin2010/article/details/69363998
7.3 Out-of-Bag评价方式
http://blog.csdn.net/qinhanmin2010/article/details/69372827
7.4 Random Patches和Random Subspaces
http://blog.csdn.net/qinhanmin2010/article/details/69373109
7.5 随机森林
http://blog.csdn.net/qinhanmin2010/article/details/69386456
7.6 Extra-Trees
http://blog.csdn.net/qinhanmin2010/article/details/69388068
7.7 特征重要程度
http://blog.csdn.net/qinhanmin2010/article/details/69390042
7.8 AdaBoost
http://blog.csdn.net/qinhanmin2010/article/details/69396152
7.9 Gradient Boosting
http://blog.csdn.net/qinhanmin2010/article/details/69488105
7.10 Stacking
http://blog.csdn.net/qinhanmin2010/article/details/69656496
7.11 练习
http://blog.csdn.net/qinhanmin2010/article/details/69659383
- Machine Learning with Scikit-Learn and Tensorflow 7 集成学习和随机森林(章节目录)
- Machine Learning with Scikit-Learn and Tensorflow 7.5 随机森林
- Machine Learning with Scikit-Learn and Tensorflow 6 决策树(章节目录)
- Machine Learning with Scikit-Learn and Tensorflow 6.4 CART算法
- Machine Learning with Scikit-Learn and Tensorflow 6.5 计算复杂度
- Machine Learning with Scikit-Learn and Tensorflow 6.8 决策树回归
- Machine Learning with Scikit-Learn and Tensorflow 6.9 决策树局限性
- Machine Learning with Scikit-Learn and Tensorflow 6.10 练习
- Machine Learning with Scikit-Learn and Tensorflow 7.1 Voting Classifiers
- Machine Learning with Scikit-Learn and Tensorflow 7.6 Extra-Trees
- Machine Learning with Scikit-Learn and Tensorflow 7.8 AdaBoost
- Machine Learning with Scikit-Learn and Tensorflow 7.9 Gradient Boosting
- Machine Learning with Scikit-Learn and Tensorflow 7.10 Stacking
- Machine Learning with Scikit-Learn and Tensorflow 7.11 练习
- Machine Learning with Scikit-Learn and Tensorflow 7.2 Bagging和Pasting
- Machine Learning with Scikit-Learn and Tensorflow 7.4 Random Patches和Random Subspaces
- Machine Learning with Scikit-Learn and Tensorflow 6.6 基尼不纯度/熵
- Machine Learning with Scikit-Learn and Tensorflow 6.7 正则化超参数
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