Yihui Xie

cv.nfeaturesLDA()

Cross-validation to find the optimum number of features (variables) in LDA

Yihui Xie & Lijia Yu / 2017-04-04


This function provids an illustration of the process of finding out the optimum number of variables using k-fold cross-validation in a linear discriminant analysis (LDA).

For a classification problem, usually we wish to use as less variables as possible because of difficulties brought by the high dimension.

The selection procedure is like this:

Note that \(g_{max}\) is set by ani.options('nmax') (i.e. the maximum number of features we want to choose).

library(animation)
ani.options(nmax = 10)
par(mar = c(3, 3, 0.2, 0.7), mgp = c(1.5, 0.5, 0))
cv.nfeaturesLDA(pch = 19)
## Loading required namespace: MASS

This animation provides an illustration of the process of finding out the optimum number of variables using k-fold cross-validation in a linear discriminant analysis (LDA).