Mastering Machine Learning with R - Second Edition
by Cory Lesmeister, Doug Ortiz, Vikram Dhillon, Miroslav Kopecky
Rotation
Should you rotate or not? As stated previously, rotation helps in the interpretation of the principal components by modifying the loadings of each variable. 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 your data, not test some hypothesis. In short, rotation aids ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access