Interpret the results. Discriminant analysis is used to classify observations into two or more groups if you have a sample with known groups. Discriminant analysis is a multivariate statistical tool that generates a discriminant function to predict about the group membership of sampled experimental data. c. Classes – This is the number of levels found in the grouping variable of interest. Discriminant analysis is used to predict the probability of belonging to a given class (or category) based on one or multiple predictor variables. Discriminant analysis builds a predictive model for group membership. On the other hand, in the case of multiple discriminant analysis, more than one discriminant function can be computed. The model is composed of a discriminant function (or, for more than two groups, a set of discriminant functions) based on linear combinations of the predictor variables that provide the best discrimination between the groups. For example, discriminant analysis helps determine whether students will go to college, trade school or discontinue education. In addition, discriminant analysis is used to determine the minimum number of dimensions needed to describe these differences. Version info: Code for this page was tested in SAS 9.3. There are many examples that can explain when discriminant analysis fits. Discriminant analysis finds a set of prediction equations, based on sepal and petal measurements, that classify additional irises into one of these three varieties. Linear Discriminant Analysis (LDA) is a well-established machine learning technique and classification method for predicting categories. Linear discriminant function analysis (i.e., discriminant analysis) performs a multivariate test of differences between groups. For example, an educational researcher interested … 2 $\begingroup$ Linear discriminant score is a value of a data point by a discriminant, so don't confuse it with discriminant coefficient, which is like a regressional coefficient. Example for Discriminant Analysis. Here Iris is the dependent variable, while SepalLength, SepalWidth, PetalLength, and PetalWidth are the independent variables. $\endgroup$ – ttnphns Feb 22 '14 at 7:51. Its main advantages, compared to other classification algorithms such as neural networks and random forests, are that the model is interpretable and that prediction is easy. Stepwise Discriminant Analysis Probably the most common application of discriminant function analysis is to include many measures in the study, in order to determine the ones that discriminate between groups. However, the main difference between discriminant analysis and logistic regression is that instead of dichotomous variables, discriminant analysis involves variables with more than two classifications. It works with continuous and/or categorical predictor variables. Previously, we have described the logistic regression for two-class classification problems, that is when the outcome variable has two possible values (0/1, no/yes, negative/positive). Variables – This is the number of discriminating continuous variables, or predictors, used in the discriminant analysis. The major distinction to the types of discriminant analysis is that for a two group, it is possible to derive only one discriminant function. The Summary of Classification table shows the proportion of observations correctly placed into their true groups by the model. The school administrator uses the results to see how accurately the model classifies the students. 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