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Decision_tree

底层开写决策树机器学习算法 一、决策树工作原理 1.1 定义 决策时(Decislon Tree)是一种非参数的有监督学习方法,它能够从一系列有特征和标签的数据中总结出决策规则。并用树状图的结构来呈现这些规则,**以解决分类和回归问题。**决策树算法的本质是一种图结构, 我们只需要问一系列问题就可以对数据进行分类。

举例:动物类别分类 已知物种以及所属类别的数据,目标获得哺乳类和非哺乳类。

决策树算法:

如果,我们发现了一种新生物Python,它是冷血动物,体表带鳞片,并且不是太升,我们可以通过决策树判断类别。 ———————————————— 版权声明:本文为CSDN博主「oax_knud」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。 原文链接:https://blog.csdn.net/oax_knud/article/details/118826373

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