Does LDA work with categorical variables?

Does LDA work with categorical variables?

LDA works on continuous variables. If the classification task includes categorical variables, the equivalent technique is called the discriminant correspondence analysis.

How do you implement linear discriminant analysis?

Linear Discriminant Analysis can be broken up into the following steps:

1. Compute the within class and between class scatter matrices.
2. Compute the eigenvectors and corresponding eigenvalues for the scatter matrices.
3. Sort the eigenvalues and select the top k.

Can the scaling values in a linear discriminant?

Can the scaling values in a linear discriminant analysis (LDA) be used to plot explanatory variables on the linear discriminants? Using a biplot of values obtained through principal component analysis, it is possible to explore the explanatory variables that make up each principle component.

How is discriminant analysis used in explanatory frameworks?

Discriminant Analysis (DA) is a statistical method that can be used in explanatory or predictive frameworks: 1 Check on a two or three-dimensional chart if the groups to which observations belong are distinct; 2 Show the properties of the groups using explanatory variables; 3 Predict which group a new observation will belong to.

How are three axes used in linear discriminant analysis?

If there are three explanatory variables- X1, X2, X3, LDA will transform them into three axes — LD1, LD2 and LD3. These three axes would rank first, second and third on the basis of the calculated score.

When to use quadratic or linear discriminant analysis?

Two models of Discriminant Analysis are used depending on a basic assumption: if the covariance matrices are assumed to be identical, linear discriminant analysis is used. If, on the contrary, it is assumed that the covariance matrices differ in at least two groups, then the quadratic discriminant analysis should be preferred.