In the field of
machine learning, the goal of
statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A
linear classifier achieves this by making a classification decision based on the value of a
linear combination of the characteristics. An object's characteristics are also known as
feature values and are typically presented to the machine in a vector called a
feature vector. Such classifiers work well for practical problems such as
document classification, and more generally for problems with many variables (
features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.