In
pattern recognition and
machine learning, a
feature vector is an n-dimensional
vector of numerical
features that represent some object. Many
algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, when representing texts perhaps term occurrence frequencies. Feature vectors are equivalent to the vectors of
explanatory variables used in
statistical procedures such as
linear regression. Feature vectors are often combined with weights using a
dot product in order to construct a
linear predictor function that is used to determine a score for making a prediction.