[This article was first published on poissonisfish, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

My last entry introduces principal component analysis (PCA), one of many unsupervised learning tools. I concluded the post with a demonstration of principal component regression (PCR), which essentially is a ordinary least squares (OLS) fit using the first $k$ principal components (PCs) from the predictors. This brings about many advantages:

1. There is virtually no limit for the number of predictors. PCA will perform the decomposition no matter how many variables you handle. Simpler models such as OLS do not cope with more predictors than observations (i.e.

# Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts.(You will not see this message again.)

Click here to close (This popup will not appear again)