贝叶斯网络的混淆矩阵
我试图理解贝叶斯网络。我有一个数据文件，有10个属性，我想获取这个数据表的混淆表，我想我需要计算所有字段的tp，fp，fn，tn。是真的吗？如果这就是我需要为贝叶斯网络做的事情。
真的需要一些指导，我迷路了。
I'm trying to understand bayesian network. I have a data file which has 10 attributes, I want to acquire the confusion table of this data table ,I thought I need to calculate tp,fp, fn, tn of all fields. Is it true ? if it's then what i need to do for bayesian network.
Really need some guidance, I'm lost.
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该过程通常是这样的：
你想用它来训练
分类器，以便它可以预测
新的未标记实例的类。
选择（神经网络，贝叶斯
net、SVM 等...）我们构建了一个
使用您的训练数据进行建模
作为输入。
来评估性能
部署之前的模型。所以使用一个
以前未使用的数据子集
（测试集），我们比较模型
这些实例的分类
与实际班级的情况相反。一个
总结这些结果的好方法
是通过混淆矩阵显示
每类实例的情况如何
预测。
对于二元分类任务，惯例是将一个类指定为正类，将另一个类指定为负类。因此，根据混淆矩阵，正确分类为阳性的阳性实例的百分比称为真阳性 (TP) 率。其他定义遵循相同的约定......
The process usually goes like this:
which you want to use to train a
classifier, so that it can predict
the class of new unlabeled instances.
of choice (neural networks, bayes
net, SVM, etc...) we build a
model with your training data
as input.
to evaluate the performance of the
model before deploying it. So using a
previously unused subset of the data
(test set), we compare the model
classification for these instances
against that of the actual class. A
good way to summarize these results
is by a confusion matrix which shows
how each class of instances is
predicted.
For binary classification tasks, the convention is to assign one class as positive, and the other as negative. Thus from the confusion matrix, the percentage of positive instances that are correctly classified as positive is know as the True Positive (TP) rate. The other definitions follows the same convention...
混淆矩阵用于评估分类器的性能，任何分类器。
Confusion matrix is used to evaluate the performance of a classifier, any classifier.
您要求的是一个具有两个以上类别的混淆矩阵。
以下是操作步骤：
类中的文档集（正标签）及其
补语（负标签）。
最大置信度值，或最大概率
这是论文的参考，您可以了解更多信息：
What you are asking is a confusion matrix with more than two classes.
Here is the steps how you do:
the set of documents in the class (positive labels) and its
complement (negative labels).
maximum confidence value, or the maximum probability
Here is the reference for the paper you can have more information: