PLS is not the same as PCR, right? Why did you expect them to perform
the same?
LinearRegression is just calling scipy.linalg.lstsq
https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lstsq.html
as you can see here:
https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/linear_model/base.py#L539
I expect that produces the minimum norm solution though I'm not familiar
with the exact solver.
On 04/16/2017 01:15 PM, Joshua Mannheimer wrote:
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
jmann...@rams.colostate.edu <mailto:jmann...@rams.colostate.edu>
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