Evaluation of binary classifiers

Evaluation of a <a href="/facts/Binary_classification/MeYYAMwp">binary classifier</a> typically assigns a numerical value, or values, to a classifier that represent its accuracy. An example is error rate, which measures how frequently the classifier makes a mistake.
There are many metrics that can be used; different fields have different preferences. For example, in medicine <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">sensitivity and specificity</a> are often used, while in computer science <a href="/facts/Precision_and_recall/Jnmmpl8H">precision and recall</a> are preferred. 
An important distinction is between metrics that are independent of the <a href="/facts/Prevalence/6UTuTGTH">prevalence</a> or skew (how often each class occurs in the population), and metrics that depend on the prevalence – both types are useful, but they have very different properties.
Often, evaluation is used to compare two methods of classification, so that one can be adopted and the other discarded. Such comparisons are more directly achieved by a form of evaluation that results in a single unitary metric rather than a pair of metrics.

Evaluation of binary classifiers open-in-new

Evaluation of binary classifiers