Confusion matrix
Table layout for visualizing performance; also called an error matrix / From Wikipedia, the free encyclopedia
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In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix,[1] 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 row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa ā both variants are found in the literature.[2] The name stems from the fact that it makes it easy to see whether the system is confusing two classes (i.e. commonly mislabeling one as another).
It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table).