|  | ## Linear Discriminant Analysis

#### Synopsis

A linear discriminant function for binominal labels and numerical attributes.

#### Description

This operator performs a linear discriminant analysis (LDA). This method tries to find the linear combination of features which best separate two or more classes of examples. The resulting combination is then used as a linear classifier. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. In the other two methods however, the dependent variable is a numerical quantity, while for LDA it is a categorical variable (i.e. the class label).

LDA is also closely related to principal component analysis (PCA) and factor analysis in that both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes of data. PCA on the other hand does not take into account any difference in class.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

#### ExampleProcess