User:Indon/GATemplate
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[edit] Basic Concept
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- See also: Type I and type II errors
A classification model (classifier or diagnosis) is a mapping instances into a certain class/group. The classifier or diagnosis result can be in a real value (continuous output) in which the classifier boundary between classes must be determined by a threshold value, for instance to determine whether a person has a hypertension based on blood pressure measure, or it can be in a discrete class label indicating one of the classes.
Let us consider a two-class prediction problem (binary classification), in which the outcomes are labelled either as positive (p) or negative (n) class. There are four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said a false positive (FP). Conversely, a true negative is occured when both the prediction outcome and the actual value are n, and false negative is when the prediction outcome is N while the actual value is p.
To get an appropriate example in a real-world problem, consider a diagnostic test whether a person is positive or negative to have a certain disease. A false positive in this case is occured when the patient is tested with positive, but actually she/he does not have the disease. A false negative, on the other hand, is occured when the person is tested as healthy while he/she is actually not.
Let define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2×2 confusion matrix or contigency table, as follows:
| actual value | ||||
|---|---|---|---|---|
| p | n | total | ||
| prediction outcome |
p' | True Positive |
False Positive |
P' |
| n' | False Negative |
True Negative |
N' | |
| total | P | N | ||
