Advanced Machine Learning with R

Rotation

Should you rotate or not? As stated previously, rotation helps in the interpretation of the principal components by modifying the loading of each variable, but makes the results technically no longer a principal component. The overall variation explained by the rotated number of components will not change, but the contributions to the total variance explained by each component will change. What you will find by rotation is that the loading values will either move farther or closer to zero, theoretically aiding in identifying those variables that are important to each principal component. This is an attempt to associate a variable to only one principal component. Remember that this is unsupervised learning, so you are trying to understand ...