贝叶斯网络和朴素贝叶斯分类器有什么区别? [英] What is the difference between a Bayesian network and a naive Bayes classifier?

问题描述

贝叶斯网络和朴素贝叶斯分类器有什么区别?我注意到其中一个只是在Matlab中实现的，因为classify另一个具有整个网络工具箱.

What is the difference between a Bayesian network and a Naive Bayes classifier? I noticed one is just implemented in Matlab as classify the other has an entire net toolbox.

如果您能在回答中解释哪一个更可能提供更好的准确性，我将不胜感激(不是前提条件).

If you could explain in your answer which one is more likely to provide a better accuracy as well I would be grateful (not a pre-requisite).

推荐答案

简短的答案，如果您只想解决预测任务:请使用朴素贝叶斯(Naive Bayes).

Short answer, if you're only interested in solving a prediction task: use Naive Bayes.

贝叶斯网络(具有良好的维基百科页)在非常一般方式.如果您知道这些关系是什么，或者有足够的数据来推导它们，那么使用贝叶斯网络可能是合适的.

A Bayesian network (has a good wikipedia page) models relationships between features in a very general way. If you know what these relationships are, or have enough data to derive them, then it may be appropriate to use a Bayesian network.

朴素贝叶斯分类器是一个简单的模型，用于描述贝叶斯网络的特定类-其中所有功能都是类条件独立的.因此，某些朴素贝叶斯无法解决的问题(下面的示例).但是，它的简单性也使其易于应用，并且在许多情况下需要较少的数据即可获得良好的结果.

A Naive Bayes classifier is a simple model that describes particular class of Bayesian network - where all of the features are class-conditionally independent. Because of this, there are certain problems that Naive Bayes cannot solve (example below). However, its simplicity also makes it easier to apply, and it requires less data to get a good result in many cases.

您在学习时遇到了二进制特征x1和x2以及目标变量y = x1 XOR x2的问题.

You have a learning problem with binary features x1 and x2 and a target variable y = x1 XOR x2.

在朴素贝叶斯分类器中，必须分别处理x1和x2-因此您可以计算出"y = 1给出x1 = 1的概率"之类的信息-希望您能看到这不是很有帮助，因为x1 = 1或多或少不会使y = 1发生的可能性.由于贝叶斯网络不具有独立性，因此可以解决此问题.

In a Naive Bayes classifier, x1 and x2 must be treated independently - so you would compute things like "The probability that y = 1 given that x1 = 1" - hopefully you can see that this isn't helpful, because x1 = 1 doesn't make y = 1 any more or less likely. Since a Bayesian network does not assume independence, it would be able to solve such a problem.

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