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Confusion matrices, a key tool to evaluate machine learning algorithm performance in classification, are a statistical tool.
Contingency tables, a type of confusion matrix, are used in the evaluation of many diagnostic exams for sensitivity, specificity, positive and negative predictive values. A contingency table is an example of a confusion matrix that is made for a binary classifier.
Confusion matrices can also show the performance of models with many classes including AI classifiers for radiology studies.