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What do you understand by the confusion matrix?
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Answer / Alok Singh Raikwar
A Confusion Matrix is a table 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 provides a visual representation of how many samples were correctly and incorrectly predicted, broken down by actual class label. The confusion matrix shows the four primary error types: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).
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