When using Linear Discriminant Analysis and k nearest neighbours in classification, which is better?

Question

I am currently performing classification on the Iris dataset. I used both LDA and kNN methods to classify the data. I found both to be highly accurate and cannot decide which one is more appropriate to use? My first thought is kNN since LDA assumes the data to have a multivariate normal distribution. However, would love to know more theory behind which is better.

Answer 1

1. k-NN should run incrementally faster than LDA as you add more dimensions to your problem.

2. Also, the k-NN time complexity is pretty much insensitive to the number of classes in most implementations. LDA on the other hand has a direct dependence on that.