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A confusion matrix is also known as the error matrix, in the field of machine learning and specifically the problem of statistical classification. A confusion matrix is a table which is often used to describe the performance of a classification model or the 'classifier' on a set of test data for which the true values are known and allows the visualization of the performance of an algorithm. It also allows easy identification of confusion between classes. and is basically a summary of prediction results on a classification problem. The  key to the confusion matrix is that the number of correct and incorrect predictions are summarized with count values and broken down by each class. The matrix also shows the ways in which the classification model is confused when it makes predictions and, matrix also gives the insight not only into the errors being made by a classifier but also to the types of errors being made.