In the field of artificial intelligence, a confusion 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. 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). Outside artificial intelligence, the confusion matrix is often called the contingency table or the error matrix.
Read more about Confusion Matrix: Example, Table of Confusion
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“When heaven and earth were in confusion hurl’d
For the debated empire of the world,
Which awed with dreadful expectation lay,
Soon to be slaves, uncertain who should sway:”
—Lucretius (Titus Lucretius Carus)
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