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Example of a confusion matrix. The reader can visualize if the model is “confusing” two classes (ie, if the model is mislabeling one class as another one). In this example, the true label of 0 is predicted with 100% accuracy, the true label of 1 is predicted with 75% accuracy, the true label of 2 is predicted with 87% accuracy, and the true label of 3 is predicted with 92% accuracy.
