What is a Confusion Matrix in Machine Learning?

What is Confusion Matrix? In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix. A confusion matrix represents information about actual and classified cases produced by a classification system. Performance of such a system is commonly evaluated by demonstrating the correct and incorrect patterns classification. A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm. A confusion matrix (Kohavi and Provost, 1998) contains information about actual and predicted classifications done by a classification system. Performance of such systems is commonly evaluated using the data in the matrix. The following table shows the confusion matrix for a two class classifier. It allows easy identification of confusion between classes e.g. one class is commonly mislabeled as the other. Most performance measures are computed from the confusion matrix. The entries in the confusion matrix have the following meaning in the context of our study: TN is the number of correct predictions that an instance is negative, FP is the number of incorrect predictions…

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