I have a dataset of 40 variables and 55 samples. I want to run classification algorithm. Is this possible that I do PCA and based on which variables are more important in each principle component, use about 4-5 of my original variables?
let's assume that I choose the first two components, and the biplot I get for my pca (given that I have 3 independent variables) is like the figure below (in answer 1). Then it means that my first variable is the most important aspect of PC1 (with the higarhest negative coefficient), and my second variable is the most important variable of PC2. then in order to make a model that is easier to interpret can I use variables 1 and 2 to make my classifier?