混淆矩阵的信息标准
人们可以使用 Akaike 信息准则 (AIC) 来衡量统计模型的拟合优度，该准则考虑了用于拟合优度以及用于模型创建的参数数量。 AIC 涉及计算该模型的似然函数最大值 (L)。 给定分类模型的预测结果（表示为混淆矩阵），如何计算L？
One can measure goodness of fit of a statistical model using Akaike Information Criterion (AIC), which accounts for goodness of fit and for the number of parameters that were used for model creation. AIC involves calculation of maximized value of likelihood function for that model (L).
How can one compute L, given prediction results of a classification model, represented as a confusion matrix?
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无法从混淆矩阵计算 AIC，因为它不包含有关可能性的任何信息。 根据您使用的模型，可能可以计算似然或准似然，从而计算 AIC 或 QIC。
您正在研究的分类问题是什么？您的模型是什么？
在分类环境中，通常使用其他措施来进行 GoF 测试。 我建议阅读 Hastie、Tibshirani 和 Friedman 撰写的《统计学习的要素》，以便对这种方法有一个很好的概述。
希望这可以帮助。
It is not possible to calculate the AIC from a confusion matrix since it doesn't contain any information about the likelihood. Depending on the model you are using it may be possible to calculate the likelihood or quasi-likelihood and hence the AIC or QIC.
What is the classification problem that you are working on, and what is your model?
In a classification context often other measures are used to do GoF testing. I'd recommend reading through The Elements of Statistical Learning by Hastie, Tibshirani and Friedman to get a good overview of this kind of methodology.
Hope this helps.
Kononenko 和 Bratko 的基于信息的分类器性能评估标准正是我所要的正在寻找：
Information-Based Evaluation Criterion for Classifier's Performance by Kononenko and Bratko is exactly what I was looking for: