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.
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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.
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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.
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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.
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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
Object | ML Term |
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True Positive | Hit rate / Recall |
False Positive | False Acceptance Rate |
True Negative | Specificity |
False Negative | False Rejection Rate / Miss Rate |