I’m sure, most of you have a huge confusion in terms of True Positive, True Negative, False Positive, False Negative.
Here I'm giving a cricket example from the batsman perspective.
Here we can consider
Model: Umpire
Positive Class: Not Out
Negative Class: Out
True positive: it is an outcome where the model correctly predicts the positive class.
The umpire gives a batsman NOT OUT when he is NOT OUT.
True Negative: it is an outcome where the model correctly predicts the negative class.
The umpire gives a batsman OUT when he is OUT.
False Positive (Type I Error): it is an outcome where the model incorrectly predicts the positive class.
The umpire gives a batsman NOT OUT when he is OUT.
False Negative (Type II Error): it is an outcome where the model incorrectly predicts the negative class.
The umpire gives a batsman OUT when he is NOT OUT.
Hope it helps a lot with a simple example.
Thank you :)
