True Positive

It is an instance where the model correctly predicts the positive class i.e, the Model predicts a true value given that the answer is actually true.

  • True Positive Rate It is the ratio of the number of True Positive classifications to the actual number of positives.

    Note: Also called Hit Rate or Recall

False Positive

It is an instance where the classifier incorrectly predicts a positive class. i.e, the Model predicts a true value given that the actual answer is false.

  • False Positive Rate It is the ratio of the number of False Positive classifications to actual number of negatives.

Note: Also called False Acceptance Rate (FAR)

True Negative

It is an instance where the model correctly predicts a negative class.

  • True Negative Rate It is the ratio of the number of Negative classifications to the actual number of negatives.

Note: Also called Specificity

False Negative

It is an instance where the Model incorrectly predicts a Negative class.

  • False Negative Rate It is the ratio of the number of False Negative classifications to actual number of false negatives.

Note: Also called False Rejection Rate or Miss Rate

ObjectML Term
True PositiveHit rate / Recall
False PositiveFalse Acceptance Rate
True NegativeSpecificity
False NegativeFalse Rejection Rate / Miss Rate