Hi all, So I am trying to write a Principle Components Regression implementation in Python to match the PLS package in R. I am getting better results in R so I am trying to figure out where the discrepancy was. The data I am using is way undetermined where n_features ~ 50,000 and n_samples ~ 500 thus why PCR is necessary. Just to see what would happen I used sklearn.LinearRegression on the original 500 X 50000 dataset. I expected I would get an error message stating the the system was not solvable but it worked and I got an answer that was at least on par with the PCR solution. So I am wondering how it is possibly solving this system if anybody knows. Thanks -- Joshua D. Mannheimer M.E. Biomedical Engineering Ph.d Student Flint Animal Cancer Research Center Office: A 259 CSU Veterinary Campus Colorado State University (970)-389-3951 jmannhei@rams.colostate.edu