In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. A simpler definition is to say that a linear classifier is one whose decision boundaries are linear. 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.

If the input feature vector to the classifier is a real vector ${\displaystyle {\vec {x}}}$, then the output score is

where ${\displaystyle {\vec {w}}}$ is a real vector of weights and f is a function that converts the dot product of the two vectors into the desired output. (In other words, ${\displaystyle {\vec {w}}}$ is a one-form or linear functional mapping ${\displaystyle {\vec {x}}}$ onto R.) The weight vector ${\displaystyle {\vec {w}}}$ is learned from a set of labeled training samples. Often f is a threshold function, which maps all values of ${\displaystyle {\vec {w}}\cdot {\vec {x}}}$ above a certain threshold to the first class and all other values to the second class; e.g.,

The superscript T indicates the transpose and ${\displaystyle \theta }$ is a scalar threshold. A more complex f might give the probability that an item belongs to a certain class.

For a two-class classification problem, one can visualize the operation of a linear classifier as splitting a high-dimensional input space with a hyperplane: all points on one side of the hyperplane are classified as "yes", while the others are classified as "no".

A linear classifier is often used in situations where the speed of classification is an issue, since it is often the fastest classifier, especially when ${\displaystyle {\vec {x}}}$ is sparse. Also, linear classifiers often work very well when the number of dimensions in ${\displaystyle {\vec {x}}}$ is large, as in document classification, where each element in ${\displaystyle {\vec {x}}}$ is typically the number of occurrences of a word in a document (see document-term matrix). In such cases, the classifier should be well-regularized.

There are two broad classes of methods for determining the parameters of a linear classifier ${\displaystyle {\vec {w}}}$. They can be generative and discriminative models. Methods of the former model joint probability distribution, whereas methods of the latter model conditional density functions ${\displaystyle P({\rm {class}}|{\vec {x}})}$. Examples of such algorithms include:

The second set of methods includes discriminative models, which attempt to maximize the quality of the output on a training set. Additional terms in the training cost function can easily perform regularization of the final model. Examples of discriminative training of linear classifiers include: