Imagine you've built a spam email classifier. How would you utilize the Confusion Matrix to understand the model's performance?
- Analyze TP, FP, TN, FN to understand the type and frequency of errors
- Focus on FP and FN to understand only the mistakes made
- Focus only on TP and TN as they represent correct classifications
- nan
In spam email classification, a Confusion Matrix helps by showing True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN), thus allowing an understanding of the type and frequency of errors, not just the correct classifications.
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