A confusion matrix is a table that is used to evaluate the performance of a classification algorithm. It is often used in machine learning and other data analysis tasks, to evaluate the accuracy of a model in predicting the correct class or category for a given data point. The confusion matrix is a two-dimensional table, with the rows representing the actual classes or categories, and the columns representing the predicted classes or categories. The entries in the table represent the number of data points that were correctly or incorrectly classified by the model.
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