What is a confusion matrix?
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The confusion matrix is a matrix used to determine the performance of the classification models for a given set of test data. It can only be determined if the true values for test data are known. The matrix itself can be easily understood, but the related terminologies may be confusing. Since it shows the errors in the model performance in the form of a matrix, hence also known as an error matrix.
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A table that summarizes how a supervised learning algorithm is performed is known as a confusion matrix. It gives an overview of the outcomes of a classification problem prediction. The confusion matrix can be used to identify both the types and quantities of errors made by the predictor.
