Hi Nicolas, Nicolas Hug wrote: > We do have CCA and other PLS-related transformers / regressors in > scikit-learn. They are able to do dimensionality reduction on both > X and Y (which I believe correspond to spp and env), so you might > want to have a look at these. However, they're not fully > compatible with the whole ecosystem unfortunately: for example our > Pipeline objects assume that only X can be transformed, not Y.
Just to clarify, I'm only seeing canonical *correlation* analysis and not canonical *correspondence* analysis (ter Braak) in scikit-learn? https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html But your point is taken - I can use this for inspiration because it has both X and Y matrices. But if I'm understanding correctly, there is no way to couple this with a further step of NearestNeighbors into a pipeline? I will only need the transformed scores coming out of CCA to feed into the NearestNeighbors step. Sorry if I'm not understanding this correctly. matt _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
