在人工智能中，混淆矩阵（英语：confusion matrix）是可视化工具，特别用于监督学习，在无监督学习一般叫做匹配矩阵。矩阵的每一列代表一个类的实例预测，而每一行表示一个实际的类的实例。基于实际的名称可以更容易判断机器是否将两个不同的类混淆了。在机器学习领域，混淆矩阵通常被称为列联表或误差矩阵。

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is usually called a matching matrix). Each column of the matrix represents the instances in a predicted class while each row represents the instances in an actual class (or vice-versa). The name stems from the fact that it makes it easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).