问题描述
基本上我的决策树无法使用常规算法对值进行分类。
Basically my decision tree can't classify a value using the normal algorithm.
我到达一个节点，有两种选择（例如，晴天和刮风），但是在这个节点上，我的值是不同的（例如，下雨天）。
I get to a node, and there are two options (say, sunny and windy), but at this node my value is different (for example, rainy).
有没有什么方法可以解决这个问题，例如更改树还是仅根据其他数据进行估计？
Are there any methods to deal with this, e.g. change the tree or just estimate based on other data?
我当时正在考虑在该节点上分配最常见的值，但这只是一个猜测。
I was thinking of assigning the most common value at that node but this is just a guess.
推荐答案
您是否考虑过表示富裕/贫乏的连续体？至于无法表达为连续体的事物，我想不出可以做到的方式。例如，阴雨天气与晴天和大风天气在我们的体验和反应方式上有根本不同，我不确定您如何期望计算机（或您正在编写决策树的任何内容）来解决问题出去做什么。 （除了简单地具有我不知道该怎么办的输出状态外，我还假设您想要的是比这更有意义的东西。）
Have you considered fuzzy logic for the rich/poor continuum? As for things that can't be expressed as a continuum, I can't think of a way it can be done. Rainy weather, for example, is so fundamentally different from sunny and windy weather in how we experience and react to it, I'm not sure how you expect a computer (or whatever it is you're writing your decision tree for) to figure out what to do. (Aside from simply having an "I don't know what to do" output state, but I'm assuming you wanted something more meaningful than that.)
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