User:Indon/GATemplate

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[edit] Basic Concept

Terminology and derivations
from a confusion matrix

true positive (TP) eqv. with hit true negative (TN) eqv. with correct rejection false positive (FP) eqv. with false alarm, Type I error false negative (FN) eqv. with miss, Type II error true positive rate (TPR) eqv. with hit rate, recall, sensitivity TPR = TP / P = TP / (TP + FN) false positive rate (FPR) eqv. with false alarm rate FPR = FP / N = FP / (FP + TN) accuracy (ACC) ACC = (TP + TN) / (P + N) specificity (SPC) SPC = TN / (FP + TN) = 1 − FPR positive predictive value (PPV) eqv. with precision PPV = TP / (TP + FP) negative predictive value (NPV) NPV = TN / (TN + FN)

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