您将如何解释整体树模型? [英] How would you interpret an ensemble tree model?

问题描述

在机器学习中，集成树模型(例如随机森林)很常见.该模型由一组所谓的决策树模型组成.但是，我们如何分析这些模型具体学到了什么呢?

In machine learning ensemble tree models such as random forest are common. This models consist of an ensemble of so called decision tree models. How can we analyse, however, what those models have specifically learned?

推荐答案

从这种意义上讲，您不能仅绘制简单的决策树.只有极简单的模型才能轻松研究.更复杂的方法需要更复杂的工具，这些工具仅是近似值，是所寻找内容的一般思路.因此，对于合奏，您可以尝试查看单个模型的一些期望值.例如，您可以寻找一些特征重要性度量，以向您显示哪些特征用于进行相同程度的预测.您不会得到一个简单的if/else结构，这根本不可能，但是有些模糊的想法.对于RF，您可以去除特征重要性，它或多或少是实际命中"考虑特定特征的决策节点的样本的预期比例.

You cannot in this sense in what you can just plot simple decision tree. Only extremely simple models can be easily investigated. More complex methods require more complex tools, which are just approximations, general ideas of what to look for. So for ensembles you can try to look at some expectation of property of a single model. For example you can look for some feature importances measures, which shows you which features are used to make prediction to same extent. You will not get a simple if/else structure, this is simply impossible, but some fuzzy idea. For RF you can take out feature importances which is more or less expected fraction of samples which actually "hit" a decision node considering a particular feature.

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