Question - Explain False Negative, False Positive, True Negative, and True Positive with a simple example.
Answer -
True Positive (TP): When the Machine Learning model correctly predicts the condition, it is said to have a True Positive value.
True Negative (TN): When the Machine Learning model correctly predicts the negative condition or class, then it is said to have a True Negative value.
False Positive (FP): When the Machine Learning model incorrectly predicts a negative class or condition, then it is said to have a False Positive value.
False Negative (FN): When the Machine Learning model incorrectly predicts a positive class or condition, then it is said to have a False Negative value.