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When evaluating a classifier's performance, it's crucial to understand the different outcomes of predictions and how they are categorized. A confusion matrix helps break down these predictions into four distinct categories: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN). Understanding these categories allows us to assess the model's ability to correctly classify instances and identify areas needing improvement.
The goal is to use the confusion matrix to: