scipy.linalg.leastq uses an SVD solver and drops singular components, where singular depends on the condition number threshold.
So it's equivalent to PCR with a tiny threshold for dropping components (rcond < 1e-15, if it's similar to numpy). SVD/rcond is on original, not on standardized variables. The PCA in PCR is not supervised in contrast to PLS (at least in general, I have no idea about scikit-learn versions) Josef On Tue, Apr 25, 2017 at 12:40 PM, Andreas Mueller <t3k...@gmail.com> wrote: > 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 > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
