Discriminant function analysis is used to predict group membership based on a linear combination of interval predictor variables. The procedure begins with a set of observations, whereby both group membership and the values of the predictor variables are known, with the end result being a linear combination of the interval variables that allows prediction of group membership. The way in which the interval variables combine allows a greater understanding and simplification of a multivariate data set. Discriminant analysis, based on matrix theory, is an established technology that has the advantage of a clearly defined decision-making process. Machine learning techniques such as neural networks may be used alternatively for predicting group membership from similar data, often with more accurate predictions, as long as the statistician is ...
Looks like you do not have access to this content.