Answer / Mohammad Ubaid

1. True Positive (TP): It occurs when the model correctly predicts a positive instance as positive. For example, if a tumor detection algorithm identifies a malignant tumor correctly, it results in a true positive.
2. True Negative (TN): It occurs when the model correctly predicts a negative instance as negative. For example, if a spam filter accurately identifies a non-spam email as non-spam, it results in a true negative.
3. False Positive (FP): It occurs when the model incorrectly predicts a negative instance as positive. For example, if a spam filter mistakenly marks a legitimate email as spam, it results in a false positive.
4. False Negative (FN): It occurs when the model incorrectly predicts a positive instance as negative. For example, if a tumor detection algorithm fails to identify a malignant tumor, it results in a false negative.

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