Definition (classification Context)
For classification tasks, the terms true positives, true negatives, false positives, and false negatives (see also Type I and type II errors) compare the results of the classifier under test with trusted external judgments. The terms positive and negative refer to the classifier's prediction (sometimes known as the expectation), and the terms true and false refer to whether that prediction corresponds to the external judgment (sometimes known as the observation). This is illustrated by the table below:
| actual class (observation) |
||
|---|---|---|
| predicted class (expectation) |
tp (true positive) Correct result |
fp (false positive) Unexpected result |
| fn (false negative) Missing result |
tn (true negative) Correct absence of result |
|
Precision and recall are then defined as:
Recall in this context is also referred to as the True Positive Rate or Sensitivity, and precision is also referred to as Positive predictive value (PPV); other related measures used in classification include True Negative Rate and Accuracy. True Negative Rate is also called Specificity.
Read more about this topic: Precision And Recall
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