Question

Explain True Positive, True Negative, False Positive, and False Negative in Confusion Matrix with an example.

Answer 1

True Positive

When a model correctly predicts the positive class, it is said to be a true positive.

For example, Umpire gives a Batsman NOT OUT when he is NOT OUT.

True Negative

When a model correctly predicts the negative class, it is said to be a true negative.

For example, Umpire gives a Batsman OUT when he is OUT.

False Positive

When a model incorrectly predicts the positive class, it is said to be a false positive. It is also known as 'Type I' error.

For example, Umpire gives a Batsman NOT OUT when he is OUT.

False Negative

When a model incorrectly predicts the negative class, it is said to be a false negative. It is also known as 'Type II' error.

For example, Umpire gives a Batsman OUT when he is NOT OUT.