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github-snooze-button-master

于 2018-10-14 发布 文件大小:23KB
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下载积分: 1 下载次数: 0

代码说明:

  Lets you add labels to Github issues that auto-clear on new activity.

文件列表:

github-snooze-button-master, 0 , 2016-04-11
github-snooze-button-master\.coveragerc, 27 , 2016-04-11
github-snooze-button-master\.gitignore, 741 , 2016-04-11
github-snooze-button-master\.travis.yml, 182 , 2016-04-11
github-snooze-button-master\LICENSE, 1056 , 2016-04-11
github-snooze-button-master\README.md, 4465 , 2016-04-11
github-snooze-button-master\code_of_conduct.md, 3222 , 2016-04-11
github-snooze-button-master\setup.cfg, 28 , 2016-04-11
github-snooze-button-master\setup.py, 991 , 2016-04-11
github-snooze-button-master\snooze, 0 , 2016-04-11
github-snooze-button-master\snooze\__init__.py, 126 , 2016-04-11
github-snooze-button-master\snooze\callbacks.py, 3072 , 2016-04-11
github-snooze-button-master\snooze\config.py, 2524 , 2016-04-11
github-snooze-button-master\snooze\constants.py, 159 , 2016-04-11
github-snooze-button-master\snooze\deploy_lambda.py, 7074 , 2016-04-11
github-snooze-button-master\snooze\lambda_handler.py, 731 , 2016-04-11
github-snooze-button-master\snooze\repository_listener.py, 6396 , 2016-04-11
github-snooze-button-master\snooze\snooze.py, 1681 , 2016-04-11
github-snooze-button-master\snooze\test, 0 , 2016-04-11
github-snooze-button-master\snooze\test\github_responses.py, 84370 , 2016-04-11
github-snooze-button-master\snooze\test\test_callbacks.py, 8616 , 2016-04-11
github-snooze-button-master\snooze\test\test_repository_listener.py, 3899 , 2016-04-11
github-snooze-button-master\snooze\test\test_snooze.py, 1674 , 2016-04-11
github-snooze-button-master\snooze\version.py, 27 , 2016-04-11
github-snooze-button-master\tox.ini, 670 , 2016-04-11

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  • 随机森林
    说明:  用N来表示训练用例(样本)的个数,M表示特征数目。 输入特征数目m,用于确定决策树上一个节点的决策结果;其中m应远小于M。 从N个训练用例(样本)中以有放回抽样的方式,取样N次,形成一个训练集(即bootstrap取样),并用未抽到的用例(样本)作预测,评估其误差。 对于每一个节点,随机选择m个特征,决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征,计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝,这有可能在建完一棵正常树状分类器后会被采用)。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)
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