Jake Hoare explains Linear Discriminant Analysis:

Linear Discriminant Analysis takes a data set of

cases(also known asobservations) as input. For each case, you need to have acategorical variableto define theclassand severalpredictor variables(which are numeric). We often visualize this input data as amatrix,such as shown below, with each case being a row and each variable a column. In this example, the categorical variable is called “class” and the predictive variables (which are numeric) are the other columns.

Following this is a clear example of how to use LDA. This post is also the second time this week somebody has suggested The Elements of Statistical Learning, so I probably should make time to look at the book.

Kevin Feasel

2017-10-13

Data Science, R