Editing Feature (machine learning) (section)

==Classification==

A numeric feature can be conveniently described by a feature vector. One way to achieve [[binary classification]] is using a [[linear predictor function]] (related to the [[perceptron]]) with a feature vector as input. The method consists of calculating the [[Dot product|scalar product]] between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold.
 
Algorithms for classification from a feature vector include [[k-nearest neighbor algorithm|nearest neighbor classification]], [[Artificial neural network|neural networks]], and [[statistical classification|statistical techniques]] such as [[Bayesian inference|Bayesian approaches]].