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

Data Science, AI and ML
#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.